Spatial and single-cell transcriptomics reveal cellular heterogeneity and a novel cancer-promoting Treg cell subset in human clear-cell renal cell carcinoma

Xiyu Song; Yumeng Zhu; Wenwen Geng; Jianhua Jiao; Hongjiao Liu; Ruo Chen; Qian He; Lijuan Wang; Xiuxuan Sun; Weijun Qin; Jiejie Geng; Zhinan Chen

doi:10.1136/jitc-2024-010183

. 2025 Jan 4;13(1):e010183. doi: 10.1136/jitc-2024-010183

Spatial and single-cell transcriptomics reveal cellular heterogeneity and a novel cancer-promoting Treg cell subset in human clear-cell renal cell carcinoma

Xiyu Song ^1,^2,⁰, Yumeng Zhu ^1,⁰, Wenwen Geng ^3,⁰, Jianhua Jiao ^2,⁴, Hongjiao Liu ¹, Ruo Chen ¹, Qian He ¹, Lijuan Wang ¹, Xiuxuan Sun ¹, Weijun Qin ^4,^✉, Jiejie Geng ^1,^2,^5,^*, Zhinan Chen ^1,^5,^*

PMCID: PMC11748785 PMID: 39755578

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.1,3 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).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).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.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.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 (figure 1A). Patient clinical data, including sex, age, tumor size, grade, preoperative glomerular filtration rate, and ki67 and VEGFA expression, are shown in online supplemental table S1. We detected a unilateral/bilateral decline in renal function consistent with tumor progression (figure 1B, online supplemental figure S1A and 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 (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 (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 (figure 1C,D and online supplemental figure S1C; 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 (figure 1E,F, online supplemental figure S1D, E). RCC cells were identified in clusters specifically expressing CA9 (figure 1D and online supplemental figure S1C). Candidate markers for RCC cells, such as FABP6, FABP7 and ANGPTL4,⁸ were also validated in our scRNA-seq data, serving as a reference for future studies (online supplemental figure S1F). According to our single-cell isolation results, T cells were the most abundant, especially CD4⁺ T cells (figure 1G and 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 (figure 1H and 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 (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 (figure 1F,G). Moreover, we found that mast cells characterized by TPSAB1-specific expression were enriched in tumor regions, which is consistent with previous reports⁹ (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 (figure 1J,K and online supplemental figure S1I).

Next, we performed subclustering analyses for major cell types and investigated their tissue distribution preferences. Subclustering of NK cells divided them into seven clusters, including well-known CD16^bright and CD56^bright subsets and a cluster of NKT cells (online supplemental figure S2A–E and online supplemental table S2). Intriguingly, we also identified a particular keratin (KRT86 and KRT81)-expressing subset,⁵ and further studies are warranted to understand the function of this new NK cell population in the kidney (online supplemental figure S2E). In the B-cell compartment, we also identiﬁed BANK1-positive, NR4A2-positive, and GPR183-positive memory B cells and plasma cells expressing JCHAIN and IGHG1 (online supplemental figure S2F–I and online supplemental table S2). Using RNA velocity analysis, we confirmed an obvious directional trajectory from early memory B cells to late plasma cells in the tissues (online supplemental figure S2J).

Abundance and heterogeneity of meta-programs in ccRCC cells

To validate the cellular heterogeneity within the TME and further explore the tumor cell population, we defined six gene sets of co-expressed genes in each sample via non-negative matrix factorization, which can be considered different meta-programs of ccRCC cells (figure 2A and online supplemental table S3). GeneSet1 was composed of genes such as TGFBI and SERPINA1 and therefore represented an EMT-related expression profile. GeneSet2 was characterized by genes such as NAT8 and SLC17A3, representing a proximal tubule (PT) cell signature gene expression profile. The presence of the PT signature in tumor cells confirmed a previous observation that PT cells are the origin of ccRCC cells.¹⁰ GeneSet3 consisted of genes such as CLDN4 and WFDC2, which are likely associated with the invasion of tumor cells.^{11 12} Genes such as BICC1 were found in GeneSet4, indicating that this gene set may be involved in angiogenesis.¹³ Interestingly, we found that GeneSet5 was characterized by VIM and MT1X, which are also related to EMT, but this gene set differed from GeneSet1 in that it included VEGFA (figure 2B and online supplemental table S3). GeneSet6 was composed of genes that corresponded to most low-quality cells, which were not further analyzed. Next, we integrated tumor cells from the four tumors, mitigated the interpatient heterogeneity through batch effect removal, and regrouped them through subclustering and differential gene analysis (figure 2C). We mapped the gene sets of these meta-programs to the clusters of ccRCC cells and found that clusters 0, 1, 2, 3, and 4 of the ccRCC subsets conformed to the gene expression profiles of GeneSet1, GeneSet2, GeneSet5, GeneSet4, and GeneSet3, respectively (figure 2D,E; tables S2 and online supplemental figure S3). The remaining two clusters of cells did not match the characteristics of these gene sets, but they presented features representing the cell cycle (CDK1, TYMS) and the stress response (FOS, JUN) (figure 2D,E and online supplemental table S2), which is consistent with previous findings.⁵

Our data revealed that the clusters RCC0 and RCC2 represented EMT^high tumor cells, whereas RCC1 represented PT cells (figure 2D,E; tables S2 and online supplemental figure S3. RNA velocity analysis showed an obvious directional flow from RCC0 to RCC2, demonstrating a differentiation relationship between two EMT^high tumor cells (figure 2F). In comparison, the latter may have greater proliferative capacity (CDK2, CDK4, and PCNA) (figure 2G). Surprisingly, we found that RCC2 was present in almost exclusively one patient (P2) (figure 2H–J); thus, we suspected that the presence of this group of cells might be related to the characteristics of the individual patient. By examining the clinical data of the patients, we found that the VEGFA level of P2 was much greater than the normal value (160 pg/mL), which was in line with the single-cell cluster data (online supplemental figure S1A and online supplemental table S1). Therefore, we hypothesized that the presence of tumor cells that express genes consistent with those in GeneSet5 may be correlated with poor outcomes for patients with ccRCC,^{14 15} possibly with more rapid deterioration and a greater likelihood of recurrence. However, these findings also suggest that these patients are more likely to benefit from treatment with inhibitors targeting VEGF. A broader range of patients is needed to verify these conclusions. Moreover, the spatial transcriptome results revealed that EMT^high tumor cells (RCC0 and RCC2) were more abundant at the IF, which shows that tumor cells at this location are undergoing EMT and are in a much more invasive and migratory state (figure 2J).

In addition, RCC1, RCC3 and RCC4 cells were mostly distributed in tissues other than tumors (figure 2J). In particular, RCC4 was abundantly distributed in AN and DN tissues. After mapping to spatial location, it was statistically found that the number of RCC4 cells was greater in AN than in DN tissues (figure 2K). Furthermore, TGFBR1, TGFBR2, IL1R1 and other cytokine receptors were highly expressed on RCC4 cells (figure 2G), implying that protumor cytokines secreted in the TME may preferentially act on RCC4 cells, increasing their likelihood of transforming into cancer cells, thus accelerating tumor invasion and promoting tumor malignancy. This finding is consistent with the notion that the tissue surrounding a tumor is not completely normal tissue.¹⁶

Regional characterization and heterogeneity of stromal cells in ccRCC

Considering the important role of stromal cells in tumor progression and metastasis, we thoroughly analyzed stromal cells in our study, which are largely under-reported in ccRCC. Moreover, we elucidated the distribution of newly identified cell subpopulations at the spatial transcriptome level, which has not been reported previously.

We identified eight clusters of fibroblasts, almost all of which were positive for ACTA2 (except cluster 4), a marker of activated fibroblasts (figure 3A, online supplemental figure S3A, B; online supplemental table S2). Cluster 7 (CD24⁺IL32⁺) showed comparable enrichment in the tumor regions and normal tissues, and cluster 4 (PTGDS⁺) was almost entirely distributed in the AN tissue. The other fibroblasts subsets were also more enriched in the tumor tissue than in normal tissue (figure 3B and online supplemental figure S3C). This distribution pattern of fibroblast populations was also observed in spatial transcriptomic data (figure 3C). A MYH11⁺ cancer-associated fibroblast (CAFs) subset (cluster 2) expressed smooth muscle cell markers (TAGLN and MYH11), which are related to smooth muscle contraction and myogenesis (online supplemental figure S3A). Moreover, we found that a collagen-expressing subset (cluster 5) highly expressed unique collagens, fibulin (FBLN2 and FBLN5), and other extracellular matrix (ECM) molecules, which participate in ECM formation and EMT and are potentially enriched in the IF (figure 3C,D and online supplemental figure S3A). Notably, we identified a group of IL1R1⁺ CAFs (cluster 0) (figure 3D), which have been reported in the TME of colorectal cancer and are linked to immune evasion, increased tumor growth and decreased survival.¹⁷ The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that cancer-related pathways were the top enriched pathways in IL1R1⁺CAFs (online supplemental figure S3D). Kaplan-Meier analysis of kidney renal clear cell carcinoma (KIRC) data from The Cancer Genome Atlas (TCGA) revealed that high expression of IL1R1⁺ACTA2⁺ cells was associated with reduced disease-free survival in the ccRCC cohort (online supplemental figure S3E). These results indicate that the IL1R1⁺CAF subset also exhibits a tumor-promoting phenotype in ccRCC.

In addition to fibroblasts, another important type of tumor stromal cell is ECs, which were categorized into seven different clusters in our study (figure 3E,F and online supplemental figure S3F; online supplemental table S2). Almost all ECs were positive for FLT1, a canonical marker of vascular ECs⁸ (online supplemental figure S3G). In the EC compartment, four clusters were mostly enriched in tumor tissues (clusters 0, 2, 3, and 6, SPARCL1⁺, IGFBP7⁺, CCL5⁺, and FABP4⁺, respectively), whereas cluster 1 (CRHBP⁺), cluster 4 (DNASE1L3⁺) and cluster 5 (SERPINE2⁺) were enriched in normal tissues (figure 3F and online supplemental figure S3H). Each cluster contained cells from different patient-derived samples, and the spatial transcriptome data confirmed the spatial distribution pattern of ECs (figure 3G and online supplemental figure S3H). CRHBP⁺ ECs (cluster 1) and SERPINE2⁺ ECs (cluster 5) were identified as normal tissue-derived cells,¹⁸ with the former being able to promote the growth of ccRCC cells through the upregulation of NF-κB-induced and p53-induced apoptosis and the latter having pericyte-like characteristics and participating in vascular maturation. Importantly, ECs from cluster 0 were defined as collagen ECs, which were also enriched at the IF, similar to collagen CAFs (figure 3Gand online supplemental figure S3H). Collagen ECs were the most abundant type of EC within this analysis (figure 3F) and were characterized by high expression of SPARCL1 (figure 3H), a secreted matricellular protein that has been reported to enhance the neoangiogenic and permeability features of tumors.¹⁹ In addition, the NOTCH and RAS signaling signatures were enriched in collagen ECs (online supplemental figure S3I). These results reveal the protumor properties of collagen ECs.

Above all, the spatial location of stromal cells influences their biological function, which is often related to the molecular complexity and cell–cell interactions in the TME. We confirmed that different ECM protein-producing stromal cells tend to be enriched and are colocalized at the IF, where they may perform multiple functions, including cell–cell interactions and extracellular context remodeling.

MRC1⁺FOLR2⁺ tumor-associated macrophages tend to exhibit a stronger procarcinogenic phenotype in ccRCC

Next, we focused on heterogeneous myeloid subsets and elucidated their distribution within ccRCC tumors (figure 4A). We identified a conventional dendritic cell (DC) cluster and a plasmacytoid DC cluster characterized by specific expression of CD1C and JCHAIN, respectively. Notably, we identified four macrophage clusters (clusters 0, 1, 4 and 5) on the basis of high expression of CD68 and CIQ (figure 4B). Among the four clusters, tumor-associated macrophages (TAM)1 was predominantly enriched in the TC, TAM2 and TAM4 were more enriched in the TR, and TAM3 was enriched in the TR and AN (figure 4C). This distribution pattern was also observed in the spatial transcriptomic data (figure 4D). On the basis of the expression of ITGAE and CD69, which are tissue-resident markers, we identified TAM4 as a group of tissue-resident macrophages (TRMs) (figure 4E). In addition, differential gene expression analysis identified distinct markers for TAM subpopulations. TAM1 expressed Osteopontin (SPP1) and TREM2, which are involved in a variety of biological processes, such as cancer and obesity.^{20 21} TAM2 highly expressed folate receptor beta (FOLR2) and most likely represented M2-type macrophages, as reflected by the specifically high expression of M2 canonical marker genes (MRC1 and CD163). Metallothionein 1E (MT1E)-enriched TAM3 highly expressed S100A8 and S100A9, encoding calcium-binding proteins, which have been shown to promote inflammatory responses in vivo.²² TCF7L2 was also preferentially expressed in TAM3, and has been shown to be involved in the differentiation of macrophages.²³ Moreover, TAM4 highly expressed insulin-like growth factor-binding protein 7 (IGFBP7) and specifically expressed VEGFA (figure 4F,G). Previous studies have shown that VEGFA-secreting macrophages promote tumor growth.²⁴

In the macrophage lineage, RNA velocity analysis revealed an obvious directional trajectory from TAM4 to TAM2 and then to TAM1, as well as from TAM4 to TAM3 (figure 4H). As the cells in each lineage differentiate, the macrophage populations gradually moved in the spatial location from the IF to the TC (figure 4D). TAM2 appeared to be in an intermediate state of macrophage differentiation. However, when we investigated immune checkpoints on TAMs, we found that the expression of PDCD1LG2 (PD-L2), CD276 (B7-H3) and HAVCR2 (TIM3) was relatively greater in TAM2 than in other clusters (figure 4I), demonstrating the enhanced immunosuppressive properties of cells in TAM2. KEGG pathway analysis revealed that the chemokine and tumor necrosis factor signaling pathways were significantly enriched in TAM2, which have been reported to increase tumor resistance and induce EMT.²⁵ Moreover, the renal cell carcinoma pathway was enriched (figure 4J). TAM2 also specifically and highly expresses PLXDC2, a gene that promotes EMT,²⁶ and DOCK4, a gene that promotes tumor angiogenesis.²⁷ Our spatial transcriptomics data revealed that PLXDC2 was concentrated in the IF, whereas DOCK4 was expressed mostly in the TC (figure 4K), indicating that TAM2 cells in the TC and IF exert their procarcinogenic effects in a manner that promotes EMT and tumor angiogenesis, respectively. Moreover, the SCENIC analysis revealed that the genes regulated by MITF, NFIC, FOXO3, NF-κB1, ATF6, IRF8 and BCL11A were upregulated in cells from TAM2 (figure 4L). Among them, IRF8⁺ TAMs have been reported to promote T-cell exhaustion in immune-infiltrated RCC,²⁸ and the activation of NF-κB signaling in M2-like TAMs could induce tumor resistance and EMT in cholangiocarcinoma.²⁹ These results indicate that TAM2 is not only in an intermediate transition state but also presents a procarcinogenic phenotype. The different tumor-promoting effects of TAM2 may be related to the immunosuppressive TME.

Single-cell analysis reveals a group of IL1B-expressing Treg cells in ccRCC

Subclustering analysis of the CD4⁺ T-cell population revealed nine cell clusters, including CD4⁺ naïve T cell, Treg, and follicular helper T cell clusters (figure 5A). Among all the clusters, two were enriched at the IF, including the CD4⁺ naïve T cells and phosphodiesterase (PDE3B)-expressing CD4⁺ T cell clusters (figure 5B). The cytotoxic CD4⁺ T-cell population (cluster 2) was identified on the basis of the expression of GZMK and GZMH (figure 5C). Moreover, Treg cells (cluster 4) were highly enriched in tumor regions, which was confirmed at the protein level (figure 5D,E and online supplemental figure S4A). Flow cytometry analysis revealed a significantly greater percentage of FoxP3⁺ cells in TC/TR tissues than in AN and DN tissues (figure 5D and online supplemental figure S4A), and FoxP3 protein levels were also greater in Treg cells from TC and TR tissues than in those from AN and DN tissues (figure 5E). The percentage of cytotoxic T-lymphocyte associated protein 4 (CTLA-4)⁺ Treg cells in TC and TR tissues was greater than that in AN and DN tissues, and CTLA-4⁺CD4⁺Foxp3⁻ T cells were evenly distributed in all tissues (figure 5F and online supplemental figure S4B), suggesting a stronger immunosuppressive function of Treg cells in tumor tissues. Notably, we discovered tertiary lymphoid structures (TLSs) in the tumor regions, especially in the TR (online supplemental figure S4C). TLSs are immune cell aggregates that form in tissues in response to persistent immune stimulation of tumors,^{30 31} and we found that Treg cells were colocalized with these TLSs (online supplemental figure S4D). We also conducted a subclustering analysis of the CD8⁺ T-cell population, identifying various cell subpopulations, such as CD8⁺ cytotoxic, resident memory and exhausted T cells (online supplemental figure S4E–H). By conducting a splicing-based RNA velocity analysis, we identified the potential differentiation trajectories of CD8⁺ resident memory T cells, effector memory T cells, and effector T cells to effector memory CD45RA⁺ T cells in the tissues (online supplemental figure S4I), suggesting a lower proliferative capacity and reduced survival time of ccRCC-associated effector T-cell populations, which are expected to reduce antitumor immunity.³²

Next, we focused on Treg cells because they are abundant in tumor tissues and play a key role in the formation of the TME. Further analysis of Treg cells revealed six Treg cell clusters that represented different functional states, including CXCR3⁺ Treg cells, naïve Treg cells, PTPRC^hi Treg cells, Th1-like Treg cells, activated Treg cells, and terminal effector Treg cells (figure 5G). Treg cells in cluster 3 expressed several characteristic genes of Th1 cells, such as GZMK, GZMA, and NKG7.³³ The PTPRC^hi Treg cell cluster (cluster 2) was identified on the basis of elevated expression of PTPRC, IKZF2, IL2RA, and ICOS.³⁴ We identified activated Treg cells (cluster 4) highly expressing activation markers (ie, FOXP3 and CTLA-4) and naïve Treg cells (cluster 1) highly expressing IL7R (figure 5H,I and online supplemental figure S4J). Cluster 0 highly expressed CXCR3 (figure 5J), which has been proven to be a crucial chemokine receptor for immune suppression in tumors.³⁵ The cells in cluster 5 presented high levels of HLA-DR, LGALS1/3 and CD74 expression, which was consistent with the characterization of terminal effector Treg cells,³⁶ suggesting that the suppressive function of the cells in cluster 5 was enhanced (figure 5I). However, Treg cells in cluster 5 presented relatively low levels of FOXP3 combined with decreased expression of ICOS; thus, we suspected that this group of cells was in an unstable state.³⁷ Therefore, we further analyzed the expression of proinflammatory factors in cluster 5 and detected the expression of inflammatory cytokines, such as IL1B, IL18 and IL7 (figure 5J). After mapping the cell types, terminal effector Treg cells were mostly distributed at the TR (online supplemental figure S4K, 5K,L). Notably, a study revealed that at the IF of RCC, a large amount of IL-1β is secreted by TRMs, which strongly interacts with many EMT genes, suggesting that IL-1β at the IF may promote the invasion and metastasis of RCC,⁵ which prompted us to further investigate the terminal effector Treg cell population that also expresses IL1B at the IF of RCC. To investigate the presence of IL-1β⁺ Treg cells in ccRCC, flow cytometry was used to determine the percentage of IL-1β⁺ Treg cells, which was significantly greater in the IF group than in DN tissue (figure 5M and online supplemental figure S4L). To further determine the localization of IL-1β⁺ Treg cells, we performed multiplex immunofluorescence analysis of tissue sections from the TC, IF, and DN and observed that they were predominantly localized at the IF (figure 5N,O and table 1). IL-1β⁺ Treg cells have been reported in inflammatory bowel disease,³⁸ but their mechanisms of production and action remain unclear, and this is the first time that they have been found in tumors. Next, we used tissue microarrays (TMAs) and aimed to validate our findings in the broader patient population (online supplemental figure S5A,B). Using mIHC, we stained tumor tissues from 151 patients with ccRCC for CD4, FoxP3, IL-1β and the surface marker FcRγ, which characterizes terminal effector Treg cells (figure 5H and table 1). To ensure the validity of our findings, we established control groups comprising AN tissues, DN kidney tissues and healthy kidney samples, as well as tissues of other kidney cancer subtypes (including chromophobe renal cell carcinoma, papillary renal cell carcinoma, mucinous tubular and spindle cell carcinoma and nephroblastomas (Wilms tumor) (online supplemental figure S5B–D and online supplemental table S4). We found that terminal effector Treg cells are almost undetectable in the control groups compared with ccRCC tissues, indicating it is a ccRCC-specific phenomena in response to specific TME signals (online supplemental figure S5C–F).

Table 1. The strategies of multiplex immunohistochemistry.

Order	Antibody	Company	Product code	Dilution	Incubation	TSA
1	CD4	Abcam	ab133616	1:100	60 min, 37°C	690
2	Il-1 beta	HUABio	ET1701-39	1:100	60 min, 37°C	570
3	FoxP3	Fuzhou Maixin	MAB-1004	Ready-to-use	60 min, 37°C	620
4	DAPI	Phenoptics	NEL811001KT	Two drops/mL	10 min, room temperature	—
1	CD4	Abcam	ab133616	1:600	60 min, 37°C	690
2	CD68	Abcam	ab213363	1:1,000	60 min, 37°C	540
3	CD206	CST	24595S	1:800	60 min, 37°C	650
4	Il-1 beta	HUABio	ET1701-39	1:100	60 min, 37°C	520
5	FoxP3	Fuzhou Maixin	MAB-1004	Ready-to-use	60 min, 37°C	620
6	DAPI	Phenoptics	NEL811001KT	Two drops/mL	10 min, room temperature	—
1	CD4	Abcam	ab133616	1:100	60 min, 37°C	690
2	FcRγ	Abcam	ab151986	1:100	60 min, 37°C	520
3	Il-1 beta	HUABio	ET1701-39	1:100	60 min, 37°C	570
4	FoxP3	Fuzhou Maixin	MAB-1004	Ready-to-use	60 min, 37°C	620
5	DAPI	Phenoptics	NEL811001KT	Two drops/mL	10 min, room temperature	—
1	CD68	Abcam	ab213363	1:1000	60 min, 37°C	690
2	CD4	Abcam	ab133616	1:600	60 min, 37°C	540
3	CD206	CST	24595S	1:800	60 min, 37°C	650
4	FcRγ	Abcam	ab151986	1:100	60 min, 37°C	520
5	FoxP3	Fuzhou Maixin	MAB-1004	Ready-to-use	60 min, 37°C	620
6	DAPI	Phenoptics	NEL811001KT	Two drops/mL	10 min, room temperature	—

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TSAtyramide signal amplification

IL-18 promotes the production of terminal effector Treg cells with stronger immunosuppressive function via the ERK/NF-κB pathway

To further explore the role of this Treg population in the development of ccRCC, we performed in-depth analysis of spatial transcriptome data to detect the numbers of activated CD4⁺ and CD8⁺ T cells within 100 µm around various Treg cell populations and found that fewer CD4⁺ and CD8⁺ T cells existed within 100 µm around terminal effector Treg cells, which suggested that the proliferation of responder T (Tresp) cells around the terminal effector Treg cells was more strongly inhibited (figure 6A,B).

Next, we sought to explore the mechanisms underlying the production of terminal effector Treg cells so that we can further validate their function. Using RNA velocity analysis, we found that terminal effector Treg cells may differentiate from naïve Treg cells (figure 6C). Treg cells generally adopt inflammatory phenotypes in response to specific inflammatory environments^{39 40}; therefore, we analyzed cytokine receptors that were specifically or highly expressed on terminal effector Treg cells and found that the IL18 receptor (IL18R1) and the IL15 receptor (IL15RA) were highly expressed (figure 6D). We therefore hypothesized that terminal effector Treg cell production might be associated with IL-15 or IL-18. To test this hypothesis, we cultured CD4⁺CD25⁺ Treg cells from patients with ccRCC for 8 days with IL-15 or/or IL-18 (figure 6E) and found that IL-18 stimulation resulted in the upregulation of IL1B expression, whereas stimulation with IL-18 and IL-15 reversed this effect (figure 6F). These results suggest that the generation and effects of terminal effector Treg cells may be related to IL-18. We also validated the expression of IL-1β at the protein level (figure 6G and online supplemental figure S6A,B). Then, the induced IL-1β⁺Treg cells were used for an in vitro suppression assay to evaluate the function of terminal effector Treg cells under ccRCC development. CD8⁺ Tresp cells and IL-1β⁺ Treg cells (or Treg cells) were co-cultured at different ratios of 1:0.25–1:1. Co-culture with IL-1β⁺ Treg cells significantly reduced the proliferation and interferon (IFN)-γ secretion of CD8⁺ T cells when compared with conventional Treg cells, which indicates the stronger immunosuppressive function of terminal effector Treg cells (figure 6H–L).

We then sought to determine the underlying molecular mechanism of how IL-18 regulated IL-1β expression. Gene Ontology analysis showed the enrichment of positive regulation of ERK1 and ERK2 cascade (online supplemental figure S6C). In addition, NF-κB is known to be involved in the regulation of the synthesis of pro-inflammatory cytokines.^{41 42} Thus, we sought to reveal whether ERK and NK-κB pathway were involved in the regulation of the production of terminal effector Treg cells. In our experiments, we found that the treatment with an ERK activator tert-butylhydroquinone (TBHQ) or an NF-κB activator phorbol12-myristate-13-acetate (PMA) further promoted the production of IL-1β⁺ Treg cells (figure 6M). Therefore, we then aimed to determine whether the effect of IL-18 on the IL-1β⁺ Treg cells production is mediated by the activation of ERK and NK-κB signaling and the relationship between these two molecules. We found that when the Treg cells were treated with IL-18, the ERK and NF-κB phosphorylation increased compared with the cells without IL-18 treatment (figure 6N,O). There was a certain amount of NF-κB expression in Treg cells without other treatments, as Treg cells in adaptive immunity tend to regulate immune tolerance and immunosuppression through activation of the NF-κB pathway.⁴³ Furthermore, we found the ERK inhibitor U0126 also inhibited the activation of NF-κB, but the NF-κB inhibitor BAY 11–7082 did not inhibit ERK activation (figure 6N,O). Therefore, ERK acted upstream of NF-κB. In summary, these findings suggested that the production of terminal effector Treg cells was mediated by IL-18 via activation of 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⁺ TAMs

Given that the strong immunosuppressive effect of terminal effector Treg cells may disrupt the tumor immune microenvironment of ccRCC, we explored the impact of their production on the prognosis of patients with ccRCC. First, we explored the expression patterns of signature genes of terminal effector Treg cells in the ccRCC cohort (KIRC) on the basis of the Kaplan-Meier analysis of TCGA pan-cancer data. Signature genes of terminal effector Treg cells (FOXP3, IL1B and FCER1G) were significantly upregulated in tumor tissues compared with normal tissues (figure 7A and online supplemental table S2). Moreover, we found that high expression of these signature genes of terminal effector Treg cells was associated with poor overall survival in ccRCC cohorts (figure 7B). To verify this result, we divided the samples of patients with ccRCC in the TMAs containing prognostic information (n=81) into two groups based on terminal effector Treg cell infiltration, and found that the high infiltration group was associated with a lower overall survival rate (figure 7C,D). We thus speculate that these terminal effector Treg cells may have a tumor-promoting phenotype that correlates with poor prognosis.

The physical interaction of cells through membrane-bound molecules that exchange signals is central to the function of many tissues.^{44 45} To explore whether any active intercellular interactions at the IF regulate the effects of terminal effector Treg cells, we used CellPhoneDB to compute potential ligand-receptor pairs in the ccRCC TME (online supplemental figure S7A). We observed 368 interactions in TC tissue, 446 in TR tissue and 271 in AN tissue (online supplemental figure S7B). Notably, we found that macrophages presented the most interaction pairs with Treg cells in cluster 5 (figure 7E). Moreover, we detected more interactions between macrophages and other cells in the TR than in the other tissue regions (online supplemental figure S7B and online supplemental table S5). We, therefore, focused on the interactions of Treg cells in cluster 5 with macrophages to further assess the role of terminal effector Treg cells in the ccRCC TME. Our data revealed that macrophages interacted with terminal effector Treg cells through PTPRC-MRC1, CSFR1-CSF1, CCL4-CCR8, CXCL12-CXCR4 and TGFB1-TGFBR1 ligand-receptor pairs (figure 7F). In addition, terminal effector Treg cells displayed many immunosuppressive interactions (TNFRSF1B-GRN, CD74-APP, and CTLA-4-CD86) with macrophages (figure 7F), suggesting that terminal effector Treg-macrophage interactions could facilitate the immunosuppressive microenvironment in ccRCC, which may also be responsible for the more strongly inhibition of the proliferation and function of surrounding Tresp cells. On the basis of the CellPhone analysis, we found that MRC1 was specifically expressed by cells in TAM2 (figure 4G). Furthermore, receptors for TGFB1 and IL10, as well as the ligands CCL4 and TGFB1, were most highly expressed in macrophages in the TAM2 cluster, supporting a potential nexus between terminal effector Treg cells and TAM2 (MRC1⁺FOLR2⁺) in the TME (figure 7G). Immune cells rely on transient physical interactions with other populations to regulate their function. Notably, we observed that terminal effector Treg cells and macrophages in the TAM2 cluster colocalized at the IF (figures4C, D and 5K 5O). We stained tissue sections from 15 patients with ccRCC for CD68, CD206, CD4, FoxP3, and IL-1β and analyzed the proximity of each IL-1β⁺ Treg cell to the nearest CD206⁺ TAM and other macrophages (figure 7H and table 1). The results revealed that IL-1β⁺ Treg cells were closer to CD206⁺ TAMs than to other macrophages (figure 7I). Staining for the characteristic surface marker FcRγ of terminal effector Treg cells also allowed visualization of physical interactions between the two cell types (figures5H 7J and table 1). These findings further supported the existence of interactions between terminal effector Treg cells and macrophages in the TAM2 cluster.

Given the speculation about the function of macrophages in TAM2 cluster at different locations, we analyzed the top 20 ligand-receptor pairs involved in macrophage interactions with terminal effector Treg cells in the TC and TR (at a significance of less than 0.01) (online supplemental figure S7C). We found that NAMPT-INSR and TGFB1-(TGFBR1+TGFBR2) were only present in the TR but not in the TC. There is increasing evidence that NAMPT (visfatin) is associated with EMT in multiple cancer types.⁴⁶ NAMPT promotes M2 differentiation of macrophages, and its overexpression is associated with the resistance to cancer therapeutics.⁴⁷ FK866, a specific inhibitor of NAMPT, has been reported to inhibit the metastatic and invasive abilities of hepatocellular carcinoma cells by inhibiting EMT.⁴⁸ TGF-β is one of the most important protein factors in inducing EMT; it interacts with receptors to activate SMAD or other pathways, thereby controlling the EMT process.⁴⁹ It is well known that GARP (a transmembrane protein encoded by the LRRC32) is necessary for the maturation and activation of TGF-β1, which in turn maintains the function and accumulation of Treg cells, thereby suppressing cancer immunity.⁵⁰ On the cell surface, the GARP/latent TGF-β complex binds to α-β integrins (αVβ6 and αVβ8) to release the mature TGF-β peptide.^{51 52} Mature TGF-β interacts with TGF-β receptors on the cell surface via autocrine and paracrine pathways.⁵³ We induced Treg cells in vitro and used conventional Treg cells and non-Treg CD4⁺ T cells (T helper cells) as controls. The expression of LRRC32 and integrin gene ITGB8 was detected by reverse transcription-quantitative PCR. We found that the LRRC32 expression level in terminal effector Treg cells was higher than that in conventional Treg cells, which is consistent with their stronger immunosuppressive function (online supplemental figure S7D). In contrast, Th cells hardly expressed LRRC32, which is consistent with previous reports.⁵¹ However, we did not detect ITGB8 expression in terminal effector Treg cells (online supplemental figure S7D). In addition, in our single-cell data, we did not detect ITGB6 expression on TAM2 (online supplemental table S6). Therefore, TGF-β produced by terminal effector Treg cells may not be able to release mature TGF-β peptides by binding to α-β integrins. It has been reported that under some circumstances, the GARP/latent TGF-β complex may also be released from the cell surface, but further research is needed to understand how TGF-β is activated from the soluble complex.⁵³ In this case, a potential autocrine loop of TGF-β present in TAM2 may be an explanation, as it is a consensus that macrophages promote EMT by producing TGF-β.⁵⁴ In summary, these findings support the hypothesis that macrophages in the TAM2 cluster may play a pro-cancerous role in IF by promoting EMT, which further confirms the protumor function of terminal effector Treg cells that aggregate at the IF.

Importantly, we further analyzed IL18 expression in all cell compartments and found that it was highest in macrophages, especially in the TAM2 cluster (figure 7G and online supplemental figure S7E). These findings further support our hypothesis about the roles played by terminal effector Treg cells in the ccRCC TME; terminal effector Treg cells at the IF interact with TAM2 cells nearby through the secretion of TGF-β1, macrophage colony-stimulating factor 1 (M-CSF1), and IL-10, transforming TAMs residing at the IF into a protumor phenotype that triggers EMT in tumor cells. Moreover, TAM2 cells secrete chemokines such as CCL4 and CXCL12 to induce Treg migration to tumor regions and overexpress IL-18 to convert Treg cells into IL-1β⁺ terminal effector Treg cells, suppressing T-cell immunity and promoting tumor growth (figure 7K).

In addition to macrophages, where the interaction was strongest, we also investigated the interaction of terminal effector Treg cells with other cell types. Our scRNA-seq data revealed that the FN1-α4β7 complex, IL1R-IL1B, and TGFB1-TGFBR1 interactions linked fibroblasts to terminal effector Treg cells (online supplemental figure S7F). IL1R1 was specifically and highly expressed in IL1R1⁺ CAFs, which were similarly localized at the IF, supporting a potential association between terminal effector Treg cells and IL1R1⁺ CAFs in the TME (figure 3C,D and online supplemental figure S7G). Given that both TAM2 cells and IL1R1⁺ CAFs play tumor-promoting roles, further supporting our hypothesis on the function of terminal effector Treg cells in ccRCC, terminal effector Treg cells may interact with other protumorigenic cells at the IF, ultimately promoting the malignant transformation of tumor cells (figure 7K and online supplemental figure S7G).

Discussion

We used multiregion-based scRNA-seq and spatial transcriptome RNA sequencing (stRNA-seq) data from the same patients to characterize the phenotypic heterogeneity and multicellular TME of ccRCC at a relatively fine resolution. In summary, our study revealed the cellular heterogeneity of both stromal and immune cells in the ccRCC TME, which was largely related to their geographic location. These data provide a rich resource for the identification of therapeutic targets for ccRCC, as illustrated by the identified chemokines, cytokines, and cell surface receptors, which could be therapeutic not only for diagnosed patients but also for patients with ccRCC susceptibility.

The role of intertumoral heterogeneity in tumor development is a focus of current research, and methods such as scRNA-seq and high-resolution spatial transcriptomics can be used to investigate this topic directly. The TME is composed of diverse tumor compartments, in which cells interact with one another.⁵⁵ In our study, tumor cells and macrophages with different functional characteristics showed distinct distributions. For example, tumor cells with invasive functions were mostly located in non-tumor tissues, accompanied by macrophages with high expression of VEGFA (TAM4), which can help tumor cells invade and metastasize,¹⁰ suggesting that TAMs may be involved in the invasion of tumor cells. Tumor cells and TAMs with EMT characteristics (RCC0 and RCC2, TAM2 and TAM4) tended to localize at the IF, which is the leading and migrating edge of a tumor. In addition, we reported the pathogenic role of IL-1β⁺ Treg cells in ccRCC, a subpopulation of Treg cells that maintained FOXP3 expression and were proinflammatory. These Treg cells can express IL-1β to potentially promote EMT in tumor cells and were also distributed at the IF, similar to the distribution of EMT-characterized tumor cells and TAMs. These results do not indicate whether tumor cells or tumor-associated immune cells are present first in the tumor compartment, but they do indicate that the cells in the tumor compartment are functionally similar and that there is a complementary role between tumor cells and the immune microenvironment.

Although immune cells work together to resist exogenous threats, in the TME, the interaction between immune cells may promote immune escape and the formation of an immunosuppressive TME.^{56 57} In our study, IL-1β⁺ Treg cells had the potential to interact with M2-like (MRC1/CD206, MSR1/CD204, and CD163) macrophages to promote tumor growth, which was associated with low patient survival. Our data indicated that tumor-infiltrating Treg cells differentiate into IL-1β⁺ Treg cells on exposure to IL-18 and drive M2 polarization of neighboring TAMs, promoting the synthesis of IL-18 and other factors that further reinforce the IL-1β⁺ Treg cell state. Moreover, stimulation of the positive feedback loop between IL-1β⁺ Treg cells and MRC1⁺ TAMs maintained a synergistic procarcinogenic effect on both cell types. We found that both cell types in ccRCC tended to accumulate at the IF of tumor and normal tissues and that their interaction underlies the formation of an immunosuppressive TME. Future studies should explore whether other factors at the IF, such as physical tension or local interactions with stromal cells such as fibroblasts, promote the establishment or maintenance of the IL-1β⁺ Treg-M2 loop.

We believe that in addition to IL-1β, IL-18 also plays an important role in promoting tumor growth in ccRCC. This conclusion is supported by previous studies, which reported that high expression of both IL-1β and IL-18 is an independent predictor of poor prognosis in patients with local ccRCC, and the prognostic value is more pronounced in low-risk patients.⁵⁸ Moreover, IL-18 can upregulate the expression of PD-L1 in NK cells, causing NK cells to exhibit an activated phenotype and enhanced effector function; moreover, it directly inhibits the proliferation of CD8⁺ T cells in a PD-L1-dependent manner in ccRCC.⁵⁹ Our study reveals another aspect of the protumor effect of IL-18 in ccRCC: IL-18 induces the production of IL-1β⁺ Treg cells and promotes EMT in tumor cells. Therefore, inhibition of the IL-18-IL-1β axis may maximize the efficacy of therapeutic or prophylactic treatments in the early stages of the disease and immunotherapy in the advanced stages of the disease. The inhibition of IL-1β has been shown to induce tumor regression in a syngeneic murine model of RCC,⁶⁰ indicating promising therapeutic potential. Furthermore, an ongoing clinical study is evaluating the effect of the combination of canakinumab (anti-IL-1β) in patients with high-risk RCC (NCT04028245). Another clinical trial has already demonstrated the efficacy of canakinumab in patients with atherosclerosis, and compared with the placebo group, the lung cancer mortality and incidence of newly diagnosed lung cancer were significantly reduced.⁶¹ Therefore, this monoclonal antibody may also provide opportunities for the treatment of ccRCC. However, given the dual role of IL-18 in tumors, the type of cells that release IL-18 and their tissue specificity, targeting IL-18 may produce different therapeutic effects. Pleiotropy is a common characteristic of cytokines, factors such as duration of action, site of action and dose have a significant impact on their biological activity. For instance, IL-2, which is used to expand Treg cells,⁶² can stimulate CD8⁺ T cells when used at high doses,⁶³ thereby achieving tumor immunotherapy. Our results do not contradict previous findings, but rather emphasize the pleiotropic nature of IL-18 that can be optimally used for cancer immunotherapy. Having addressed the molecular, spatial, and functional regulation of IL-1β⁺ Treg cells, our study may inform the design and interpretation of clinical trials testing prophylactic or combination immunotherapy targeting IL-18.

Methods

Clinical patient samples

All the clinical samples were collected from Xijing Hospital of the Fourth Military Medical University. Information on the clinical samples is shown in online supplemental table S1. All patients were diagnosed radiologically and evaluated via histopathological examination. All 15 patients included underwent surgical resection and the diagnosis of ccRCC was confirmed by histopathological analysis. Inclusion was in accordance with the principle of randomization, and the gender factor was balanced. As this study was a multiregion sampling, we included patients who were eligible for nephrectomy rather than partial nephrectomy for the convenience of sampling and to avoid the problem of contamination between samples. Our surgical sampling procedure was as follows: the clinician cut-off the whole kidney, and in order to avoid contamination of normal tissues by tumor tissues, the samples were cut in the order of DN, IF and TC (for scRNA-seq and flow cytometry studies, we cut-off the TR (“a” in figure 1A) and the AN (“b” in figure 1A) separately, while discarding the junction between them), during which the scalpel was rinsed with saline, and then the tissues from each region were separately divided into saline or tissue storage solution.

Peripheral blood mononuclear cell isolation

Whole blood was collected in EDTA tubes and used within a few hours. 15 mL of Lymphoprep (1858, Serumwerk Bernburg AG) were prepared in 50 mL centrifuge tubes, to which 30 mL of diluted blood was added slowly. The samples were subsequently centrifuged at 400×g (with no break) for 20 min at 18–22°C. After centrifugation, a layer of peripheral blood mononuclear cells (PBMCs) was visible. PBMCs were aspirated and then washed with saline to restore normal osmolality. Red blood cells (RBCs) were lysed to prevent RBC contamination.

Preparation of single-cell suspensions

The tissues were stored in MACS Tissue Storage Solution (Miltenyi Biotec, Cat#130-100-008) and were kept on ice before use. The tissues were subsequently cut into small pieces of 2–4 mm and transferred into gentleMACS C tubes (Miltenyi Biotec, Cat#130–093–237) containing the enzyme mixture. The enzyme mixtures of the TC/TR and AN/DN kidney samples were prepared following the manufacturer’s instructions for the Tumor Dissociation Kit (Miltenyi Biotec, Cat#130-095-929) and the Multi Tissue Dissociation Kit (Miltenyi Biotec, Cat#130-110-201), respectively. The tissues were dissociated via a gentleMACS Dissociator (Miltenyi Biotec, Cat#130-093-235).

Single-cell RNA sequencing

Fresh tissues were minced and immediately digested into single-cell suspensions. CD45⁺ cells were isolated via positive magnetic selection via a Human CD45 Positive Selection Kit (STEMCELL, Cat#100–0107) following the manufacturer’s protocol. After cell quality control, single-cell libraries were constructed according to the manufacturer’s instructions. The complementary DNA (cDNA) was then recovered via magnetic beads, amplified via PCR for full-length cDNA, and subsequently purified. Quality control of the cDNA concentration was performed using a Qubit V.4.0 instrument, and quality control of the cDNA integrity was performed using an Agilent 2100 instrument. The appropriate amount of cDNA was selected for library construction, which included the basic steps of fragmentation, end repair and the addition of poly-A tails, junction ligation and index amplification. A Qubit V.4.0 instrument was used for quality control of the library concentration, and Qseq400 was used for quality control of the library fragments. The sequencing was performed using a second-generation sequencing platform.

CellPhoneDB

CellPhoneDB (V.2.0.6) used the cluster annotation and raw counts from our single-cell transcriptomics data to compute cell–cell communication between different cell types.⁶⁴ The default ligand-receptor pair information was used in this process considering only ligands and receptors with expression in more than 15% of the cell subtypes. The p values were calculated at 1000 times permutation test, and values greater than 0.05 indicated significant enrichment of the interacting ligand-receptor pair in each of the interacting pairs of two cell types.

CellChat

To enable a systematic analysis of cell–cell communication, we re-clustered distinct each cell type. The CellChat package (V.1.6.1) was adopted to explore the ligand-receptor pairs between different cell types as previously reported.⁶⁵ We chose the receptors and ligands expressed in more than 10% of the cells in the specific cluster for subsequent analysis. The interaction between distinct cell subpopulations via putative ligand-receptor pairs was visualized using ggplot2 package.

SCENIC analysis

SCENIC analysis was conducted with the pySCENIC package (V.0.9.9),⁶⁶ a lightning-fast python implementation of the SCENIC pipeline. Two gene-motif rankings (10 kb around the transcription start site (TSS) or 500 bp upstream of the TSS) were used to determine the search space around the TSS, and the 20,000 motif database was used for RcisTarget and GENIE3.

RNA velocity analysis

For the RNA velocity analysis, the scVelo R package (V.0.3.2)⁶⁷ was employed to recount spliced and unspliced reads from pre-aligned BAM files of scRNA-seq data. Following this, RNA velocity values for each gene of each cell were calculated, and the resulting RNA velocity vectors were embedded into low-dimensional space using the scVelo pipeline. Subsequently, the developmental trajectories were inferred by embedding RNA velocity vectors into the uniform manifold approximation and projection (UMAP) space.

Spatial transcriptomics sequencing

The tissues resected from patients with ccRCC were immediately dissected, washed with phosphate-buffered saline (PBS), and snap-frozen in isopentane prechilled with liquid nitrogen. The tissues were then embedded in optimum cutting temperature compound (SAKURA, Cat#4583) on dry ice and kept at −80°C until use. The sections were cut to a thickness of 10 µm and placed on gene expression slides and chilled tissue optimization slides (BMKMANU S1000). The gene expression slides were printed with one to eight identical 6.8×6.8 mm capture zones, each with 2,000,000 spots that contained barcoded primers. The primers were attached to the slide via the 5’ end and contained a cleavage site, a spot-unique spatial barcode, a T7 promoter region, a unique molecular identifier, a partial read1 Illumina handle, and Poly(dT)VN. The spots were 2.5 µm in diameter and were arranged in a centered regular-hexagonal grid with six spots around each spot and a center-to-center distance of 4.8 mm. Tissue optimization, fixation, staining and imaging were conducted following the manufacturer’s protocol. Nuclei were also stained and imaged for subsequent cell segmentation. After reverse transcription (RT) and spatial library preparation, the library was delivered for quality control and sequenced on an Illumina NovaSeq 6000 platform, generating at least 50,000 reads per spot and 150 bp paired-end reads.

Spatial transcriptomics analysis

The upstream analysis was completed through BSTMatrix (V.2.3). The GRCh38_release95 human genome served as a reference. The cell segmentation of the diamidino-2-phenylindole (DAPI)-stained images was performed via the watershed algorithm in cellpose V.2.0. The data analysis steps included normalization, clustering and screening of marker genes using the R package Seurat (V.4.0.1), followed by marker gene annotation and functional enrichment analysis using the R package clusterProfiler (V.3.18.1) and construction of the protein–protein interaction network using the STRING database, R package igraph, and R package ggraph. Finally, transcription factor-binding site prediction was performed using the R package TFBSTools, and visualization was performed using the R package ggplot2.

Isolation and culture of Treg cells

Human PBMCs were labeled with fluorochrome-conjugated antibodies against CD4 (BioLegend, OKT4, Cat#317408) and CD25 (BioLegend, M-A251, Cat#356134), and cell sorting was performed using a BD Aria III flow cytometer. Treg cells were cultured with anti-CD3/anti-CD28-coated microbeads from the T-Cell Activation/Expansion Kit (Miltenyi Biotec, Cat#130-091-441) at a 1:1 ratio for 72 hours in medium (Roswell Park Memorial Institute [RPMI]-1640) containing 10% FBS, 1% penicillin-streptomycin, 2 mM l-glutamine and 200 IU/mL IL-2 (PeproTech, Cat#200-02-100UG) in a humidified incubator at 37°C with 5% CO₂. Following 72 hours of initial stimulation, the Treg cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, 2 mM L-glutamine, 200 IU/mL IL-2 and 50 ng/mL IL-18 (BioLegend, Cat# 592102). Inhibitors and activators used in in vitro experiments include: PMA (MedChemExpress, HY-18739), TBHQ (MedChemExpress, HY-100489), U0126 (MedChemExpress, HY-12031A) and BAY 11–7082 (MedChemExpress, HY-13453). Their final concentration is 5 µM. Treg cells were passaged to fresh plates every 48 hours for a total of 8 days of stimulation, at which point the cells were used for further experiments.

In vitro suppression assay

Using a CellTrace CFSE Cell Proliferation Kit (Invitrogen, Cat#C34554), purified CD8⁺ T cells were labeled with 10 µM carboxyfluorescein diacetate succinimidyl ester (CFSE). CD8⁺ T cells (5×10⁵ (48-well plate)) were cultured with the stimulation of anti-CD3/anti-CD28-coated microbeads and 200 IU/mL IL-2, in the presence of the corresponding ratio of Treg cells for 96 hours. The positive control is CD8⁺ T alone which is absent in Treg cells and the negative control also absent stimulation of anti-CD3/anti-CD28-coated microbeads.

Flow cytometry

Foxp3 and intracellular staining were performed via the Foxp3/Transcription Factor Staining Buffer Set (eBioscience, Cat#00-5523-00) with fluorochrome-conjugated antibodies according to the suggested protocol. For IL-1β staining, the cells were stimulated with 100 ng/mL LPS (Sigma-Aldrich, Cat# L2630) for 4–6 hours and then blocked with brefeldin A (BioLegend, Cat# 420601) (1:1000) for 4 hours. The antibodies used (1:100) were CD3-APC/Fire 750 (BioLegend, UCHT1, Cat#300470), CD4-FITC (BioLegend, OKT4, Cat#317408), CD8a-PerCP (BioLegend, HIT8a, Cat#300922), FOXP3-PE (BioLegend, 259D, Cat#320208), CTLA-4-PECy7 (BioLegend, L3D10, Cat#349914), IL-1β-AF647 (BioLegend, JK1B-1, Cat#508208), Ki-67-PE (BioLegend, 11F6, Cat#151209), and IFN-γ-APC (BioLegend, B27, Cat#506510). Living cells were identified using a Zombie UV Fixable Viability Kit (BioLegend, Cat#423107). Cell analysis was performed with a BD Fortessa flow cytometer, and the data were analyzed using FlowJo (V.10.10.0) software.

Real-time quantitative PCR

For each sample, RNA was isolated according to the manufacturer’s protocol using a Total RNA Kit II (OMEGA-BIO-TEK, Cat# D6934-01). Reverse transcription was performed with PrimeScript RT Master Mix (Takara, Cat# RR036A). Quantitative PCR analysis was carried out using TB Green Premix Ex Taq II reagent, and the reactions were performed on a CFX Opus 96 Real-Time PCR System. The CT values were normalized to those of β-actin, and the relative expression levels were obtained using the 2-ΔΔCT method. The sequences of primers used were as follows:

IL1B-F: GCTTGGTGATGTCTGGTCCATAT

IL1B-R: CGCAGGACAGGTACAGATTCTTT

β-actin-F: ATCGTGCGTGACATTAAGGAGAA

β-actin-R: GTTGAAGGTAGTTTCGTGGATGC

ITGB8-F: CGTGACTTTCGTCTTGGATTTGG

ITGB8-R: TCCTTTCGGGGTGGATGCTAA

LRRC32-F: TGGTGGACAAGAAGGTCTCGT

LRRC32-R: CCCAGATAGATCAAGGGTCTCAG

Western blot analyses

Treg cells were immediately homogenized in the protein lysis buffer containing phosphatase inhibitors (Roche, Switzerland), protease inhibitors and phenylmethylsulfonyl fluoride. BCA Protein Assay Kit (Thermo Fisher Scientific) was used for quantifying the amounts of proteins. The protein samples were boiled for 10 min and electrophoresed in the sodium dodecyl sulfate (SDS)-polyacrylamide gels. The gels were then transferred onto polyvinylidene fluoride (PVDF) membranes. Membranes were blocked in the 5% fat-free milk at room temperature for 1 hour and incubated with antibodies at 4°C overnight, then incubated with horseradish peroxidase-labeled secondary antibody for 1 hour. The following antibodies were used: anti-NF-κB (Cat#8242T, 1:1000), anti-p-NF-κB (Cat#3033T, 1:1000), anti-p-ERK (Cat#4370T, 1:2000), and anti-ERK (Cat#4695T, 1:1000) (All from Cell Signaling Technology). Finally, the membranes were visualized using ECL reagents (Amersham Bioscience).

Immunohistochemistry

Sections (4 μm-thick) were placed in the oven at 60°C overnight. The slides were deparaffinized in xylene and dehydrated in a gradient series of 100%, 95%, 85%, and 75% alcohol. After blocking endogenous peroxidase activity, heat-induced antigen retrieval and incubation with blocking buffer, the slides were incubated with primary antibody (FoxP3, Fuzhou Maixin, Cat# MAB-1004, ready-to-use) at 4°C overnight. The slides were washed three times with PBS and incubated with the secondary antibody for 1 hour at room temperature. Positive staining was visualized using 3,3'-diaminobenzidine. Finally, the slides were dehydrated, mounted, and observed using the Nikon Eclipse Ni microscope (Tokyo, Japan). A 3DHISTECH digital slide scanner was used for further analysis.

Multiplex immunohistochemistry

mIHC was performed on sections (4 μm-thick) of formalin-fixed paraffin-embedded tissues using the Manual Opal 7-Color IHC Kit (Phenoptics, Cat#NEL811001KT) following the manufacturer’s protocol. Healthy kidney samples (N0021F0100-B30-03, N0023F0100-B30-N01, N0024F0100-B30-N01, N0025F0100-B30-N01, N0026F0100-B30-N01, N0027F0100-B30-N01) and TMAs (OD-CT-UrKid01-005, HKidC080PT01, OD-CT-UrKid03-003, OD-CT-UrKid04-001, HKid-CRC180Sur-01, KidE085CS01) were bought from Shanghai Outdo Biotech. Scanning images of the sections stained with multiple opal fluorophores were obtained using a PhenoImager HT scanner (Akoya Biosciences), and image analysis was performed using HALO software. The staining panels were as follows.

Statistical analysis

Statistical analyses were performed with GraphPad Prism (V.10.1.2) software. Significant differences in the tests were analyzed via one-way analysis of variance or t-tests, and all tests were two-sided. P values<0.05 were considered statistically significant. Correlation analyses were performed via Pearson analysis. All the experiments were repeated three or more times in at least three independent replicates. The sequencing was performed by Biomarker Technologies (Beijing, China), and the basic analyses of the scRNA-seq data were performed using BMKCloud (www.biocloud.net).

supplementary material

online supplemental figure 1

jitc-13-1-s001.docx^{(3.5MB, docx)}

DOI: 10.1136/jitc-2024-010183

online supplemental figure 2

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DOI: 10.1136/jitc-2024-010183

online supplemental table 1

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DOI: 10.1136/jitc-2024-010183

online supplemental table 2

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DOI: 10.1136/jitc-2024-010183

online supplemental table 3

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DOI: 10.1136/jitc-2024-010183

online supplemental table 4

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DOI: 10.1136/jitc-2024-010183

online supplemental table 5

jitc-13-1-s007.xlsx^{(13.5KB, xlsx)}

DOI: 10.1136/jitc-2024-010183

online supplemental table 6

jitc-13-1-s008.xlsx^{(2MB, xlsx)}

DOI: 10.1136/jitc-2024-010183

Footnotes

Funding: This work was supported by the National Natural Science Foundation of China (No. 82220108004; 82404055; 82173204), the fund of State Key Laboratory (20232BCD44010), the Innovation Capability Support Program of Shaanxi (2023-CX-TD-72; 2021TD-39; 2020PT-021), the Natural Science Basic Research Program of Shaanxi (2022JZ-62), and Xijing Hospital (LHJJ24JH18; LHJJ24JH19; XJZT24CY27).

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient consent for publication: Consent obtained from parent(s)/guardian(s).

Ethics approval: This study involves human participants and was approved by The Ethics Committee of Fourth Military Medical University (no. XJYY-LL-FJ-059). Participants gave informed consent to participate in the study before taking part.

Correction notice: This article has been corrected since it was first published online. The affiliation of author Wenwen Geng has been updated to: Department of Breast Surgery, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China.

Data availability statement

Data are available upon reasonable request.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

online supplemental figure 1

jitc-13-1-s001.docx^{(3.5MB, docx)}

DOI: 10.1136/jitc-2024-010183

online supplemental figure 2

jitc-13-1-s002.jpg^{(450.2KB, jpg)}

DOI: 10.1136/jitc-2024-010183

online supplemental table 1

jitc-13-1-s003.xlsx^{(11.2KB, xlsx)}

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online supplemental table 2

jitc-13-1-s004.xlsx^{(1.1MB, xlsx)}

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online supplemental table 3

jitc-13-1-s005.xlsx^{(11.6KB, xlsx)}

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online supplemental table 4

jitc-13-1-s006.xlsx^{(25.5KB, xlsx)}

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online supplemental table 5

jitc-13-1-s007.xlsx^{(13.5KB, xlsx)}

DOI: 10.1136/jitc-2024-010183

online supplemental table 6

jitc-13-1-s008.xlsx^{(2MB, xlsx)}

DOI: 10.1136/jitc-2024-010183

Data Availability Statement

Data are available upon reasonable request.

[R1] 1.Leibovich BC, Lohse CM, Crispen PL, et al. Histological subtype is an independent predictor of outcome for patients with renal cell carcinoma. J Urol. 2010;183:1309–15. doi: 10.1016/j.juro.2009.12.035. [DOI] [PubMed] [Google Scholar]

[R2] 2.Lipworth L, Morgans AK, Edwards TL, et al. Renal cell cancer histological subtype distribution differs by race and sex. BJU Int. 2016;117:260–5. doi: 10.1111/bju.12950. [DOI] [PubMed] [Google Scholar]

[R3] 3.Motzer RJ, Jonasch E, Agarwal N, et al. Kidney Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2022;20:71–90. doi: 10.6004/jnccn.2022.0001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Braun DA, Bakouny Z, Hirsch L, et al. Beyond conventional immune-checkpoint inhibition - novel immunotherapies for renal cell carcinoma. Nat Rev Clin Oncol. 2021;18:199–214. doi: 10.1038/s41571-020-00455-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.Li R, Ferdinand JR, Loudon KW, et al. Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer. Cancer Cell. 2022;40:1583–99. doi: 10.1016/j.ccell.2022.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Liu Y, He S, Wang X-L, et al. Tumour heterogeneity and intercellular networks of nasopharyngeal carcinoma at single cell resolution. Nat Commun. 2021;12:741. doi: 10.1038/s41467-021-21043-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Wu J, Chan Y-T, Lu Y, et al. The tumor microenvironment in the postsurgical liver: Mechanisms and potential targets of postoperative recurrence in human hepatocellular carcinoma. Med Res Rev. 2023;43:1946–73. doi: 10.1002/med.21967. [DOI] [PubMed] [Google Scholar]

[R8] 8.Hu J, Chen Z, Bao L, et al. Single-Cell Transcriptome Analysis Reveals Intratumoral Heterogeneity in ccRCC, which Results in Different Clinical Outcomes. Mol Ther. 2020;28:1658–72. doi: 10.1016/j.ymthe.2020.04.023. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Cheng S, Li Z, Gao R, et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell. 2021;184:792–809. doi: 10.1016/j.cell.2021.01.010. [DOI] [PubMed] [Google Scholar]

[R10] 10.Young MD, Mitchell TJ, Vieira Braga FA, et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science. 2018;361:594–9. doi: 10.1126/science.aat1699. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Agarwal R, D’Souza T, Morin PJ. Claudin-3 and claudin-4 expression in ovarian epithelial cells enhances invasion and is associated with increased matrix metalloproteinase-2 activity. Cancer Res. 2005;65:7378–85. doi: 10.1158/0008-5472.CAN-05-1036. [DOI] [PubMed] [Google Scholar]

[R12] 12.Zhuang H, Tan M, Liu J, et al. Human epididymis protein 4 in association with Annexin II promotes invasion and metastasis of ovarian cancer cells. Mol Cancer. 2014;13:243. doi: 10.1186/1476-4598-13-243. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Huang C, Li H, Xu Y, et al. BICC1 drives pancreatic cancer progression by inducing VEGF-independent angiogenesis. Signal Transduct Target Ther. 2023;8:271. doi: 10.1038/s41392-023-01478-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Patel SA, Nilsson MB, Le X, et al. Molecular Mechanisms and Future Implications of VEGF/VEGFR in Cancer Therapy. Clin Cancer Res. 2023;29:30–9. doi: 10.1158/1078-0432.CCR-22-1366. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Lawler J. Counter regulation of tumor angiogenesis by vascular endothelial growth factor and thrombospondin-1. Semin Cancer Biol. 2022;86:126–35. doi: 10.1016/j.semcancer.2022.09.006. [DOI] [PubMed] [Google Scholar]

[R16] 16.Erickson A, He M, Berglund E, et al. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature New Biol. 2022;608:360–7. doi: 10.1038/s41586-022-05023-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Koncina E, Nurmik M, Pozdeev VI, et al. IL1R1+ cancer-associated fibroblasts drive tumor development and immunosuppression in colorectal cancer. Nat Commun. 2023;14:4251. doi: 10.1038/s41467-023-39953-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Spatial and single-cell transcriptomics reveal cellular heterogeneity and a novel cancer-promoting Treg cell subset in human clear-cell renal cell carcinoma

Xiyu Song

Yumeng Zhu

Wenwen Geng

Jianhua Jiao

Hongjiao Liu

Ruo Chen

Qian He

Lijuan Wang

Xiuxuan Sun

Weijun Qin

Jiejie Geng

Zhinan Chen

Abstract

Background

Methods

Results

Conclusions

WHAT IS ALREADY KNOWN ON THIS TOPIC

WHAT THIS STUDY ADDS

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Introduction

Results

Global analysis of cell populations in ccRCC

Abundance and heterogeneity of meta-programs in ccRCC cells

Regional characterization and heterogeneity of stromal cells in ccRCC

MRC1+FOLR2+ tumor-associated macrophages tend to exhibit a stronger procarcinogenic phenotype in ccRCC

Single-cell analysis reveals a group of IL1B-expressing Treg cells in ccRCC

Table 1. The strategies of multiplex immunohistochemistry.

IL-18 promotes the production of terminal effector 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+ TAMs

Discussion

Methods

Clinical patient samples

Peripheral blood mononuclear cell isolation

Preparation of single-cell suspensions

Single-cell RNA sequencing

CellPhoneDB

CellChat

SCENIC analysis

RNA velocity analysis

Spatial transcriptomics sequencing

Spatial transcriptomics analysis

Isolation and culture of Treg cells

In vitro suppression assay

Flow cytometry

Real-time quantitative PCR

Western blot analyses

Immunohistochemistry

Multiplex immunohistochemistry

Statistical analysis

supplementary material

Footnotes

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

MRC1⁺FOLR2⁺ tumor-associated macrophages tend to exhibit a stronger procarcinogenic phenotype in ccRCC

Terminal effector Treg cells are linked to decreased survival, increased immune evasion and tumor growth via interactions with MRC1⁺FOLR2⁺ TAMs