Supplementary Figures from Mesenchymal-like Tumor Cells and Myofibroblastic Cancer-Associated Fibroblasts Are Associated with Progression and Immunotherapy Response of Clear Cell Renal Cell Carcinoma
Supplementary Figure 1. Phenotyping of main cell populations. A. Metrics (number of cells, reads, median genes, UMI) after sequencing of each sample and their associated clinical characteristics. B. Expression of common markers for tumour, immune and stromal cells and of epithelial and mesenchymal states represented as percentage of expressing cells after dividing the UMAP into 80 hexagonal bins. Supplementary Figure 2. Diversity in lymphoid, myeloid and endothelial populations. A. UMAP plot of lymphoid sub-clusters (27,223 cells). B. Pseudo-bulk heatmap of identity markers for each lymphoid sub-cluster. Abbreviations: CD4.reg: regulatory CD4-T cells; CD4.eff: effector CD4-T cells; CD4.mem: memory CD4-T cells; CD8.mem: memory CD8-T cells; CD8.eff: effector CD8-T cells; CD8.ex: exhausted CD8-T cells; CD8.stress: stressed CD8-T cells; NKT.inf: inflamed natural killer-like T cells; NKT.IFNG: NKT cells expressing IFNG as top marker; NKT.stress: stressed NKT cells; NK.HBB: natural killer cells expressing HBB as top marker; NK.PTGDS: NK cells expressing PTGDS as top marker; NK.surv: surveillance NK cells; NK.ct: cytotoxic NK cells. C. UMAP plot of endothelial sub-clusters (9,726 cells). Abbreviations: ED.cor: cortical endothelial cells; ED.lymph: lymphatic endothelial cells; ED.RGCC: endothelial cell expressing RGCC as top marker; ED.venous: venous endothelial cells; ED.glom: glomerular endothelial cells; POD: podocytes. D. Pseudo-bulk heatmap of identity markers for each endothelial sub-cluster. E. VISION projection of capillary and arterial signatures coloured by value ranks showing a division of the cortical endothelial sub-cluster. F. UMAP plot of myeloid sub-clusters showing the gradient from M1-like to M2-like macrophages (3,295 cells). Abbreviations: NEUT: neutrophils; MONO.cl: classical monocyte; MONO.at: atypical monocytes; TAM: tumour-associated macrophages; TAM.CD1C: TAM expressing CD1c as top marker. G. Pseudo-bulk heatmap of identity markers of each monocyte and macrophage sub-cluster. H. VISION projection of M1- and M2-like macrophages signatures coloured by value ranks highlighting a gradient in the TAM sub-cluster. Supplementary Figure 3. Heterogeneity in the ccRCC.int populations. A. Pie chart displaying the proportion of each ccRCC subtypes in the total tumour collection. B. UMAP projection of gene expression for TGFBI (red) and EPCAM (green) in ccRCC tumour cells. C. KEGG pathway ontology analysis of specific markers for tumour sub-clusters (number of markers for each cluster indicated in brackets; number of genes found in each pathway noted near the bar). D. VISION projection of the melanoma immune-like signature. E. GSEA analysis of ccRCC.int1 versus ccRCC.int2. F. UMAP plot showing ccRCC.int sub-clustering. Each ccRCC.int is divided into two sub-clusters (ccRCC.int1 in C0 and C2; ccRCC.int2 in C1 and C3). G. Heatmap of the 10-top markers for each sub-cluster. H. DAVID gene ontology analysis of specific markers for the four new formed sub-clusters using BP_DIRECT (number of markers for each cluster indicated in brackets; number of genes found in each pathway noted near the bar). Supplementary Figure 4. Validation of ccRCC tumour populations in dataset PRJNA705464 from Krishna et.al. A. Original labelling superimposed on the UMAP plot obtained after re-analysis of CA9-expressing cells. B. UMAP plot showing identified sub-clusters. C. Bar plot displaying the contribution of 3 tumour samples to each identified cluster. Patient samples comprising less than 20 annotated tumour cells were not included. D. Piechart displaying the global proportion of each identified cluster. E. VISION projections of indicated ccRCC signatures. F. Heatmap representation of the GSVA analysis showing specific hallmarks enriched in each cluster. G. Pseudo-bulk heatmap of 5 specific markers for epithelial, mesenchymal and inflamed-state cells. Supplementary Figure 5. Validation of ccRCC tumour populations in PRJNA671297 from Braun et.al. A. Original labelling superimposed on the UMAP plot obtained after our re-analysis and removal of misannotated cells (Dataset: on dbGaP, accession number phs002252.v1.p1). B. UMAP plot showing identified sub-clusters. C. Bar plot displaying the contribution of 5 tumour samples to each identified cluster. Patient samples comprising less than 20 annotated tumour cells were not included. D. Piechart displaying the global proportion of each identified cluster. E. VISION projections of indicated ccRCC signatures. F. Heatmap representation of the GSVA analysis showing specific hallmarks enriched in each cluster. G. Pseudo-bulk heatmap of 5 specific markers of epithelial, mesenchymal and inflamed-state cells. Supplementary Figure 6. Description of PST clusters. A. Localisation of each identified cluster with respect to nephron structure. B. Heatmap of the 10-top markers for each tubule sub-cluster. C. Differentially expressed genes between PST1 (blue) and PST2 (red). D. Pseudo-bulk heatmap of S1/S2/S3 PST segment markers by Young et.al., and Lake et.al., to assess if different segments have been captured in our dataset. E. VISION projection of gluconeogenesis, glycolysis, oxidative phosphorylation and HIF1a signatures. F. Pseudo-bulk heatmap recapitulating expression of key genes for PST and the indicated ccRCC populations. Supplementary Figure 7. Analysis of cell populations in TCGA-KIRC cohort. A. Heatmap showing deconvolution results of all identified populations inferred by CIBERSORTx and displayed as row-scaled absolute scores on bulk RNA-seq data of 495 tumour samples. B. Reduced heatmap for deconvolution results of indicated populations displayed as column-scaled CIBERSORTx absolute scores. C. Sorted Spearman correlation coefficients between the indicated ccRCC populations and all other identified populations. D. Heatmap of each subpopulation average row-scaled absolute score with samples grouped according to the m1-m4 TCGA-KIRC classification. Supplementary Figure 8. Spatial localization of tumour cells and myCAFs. A. Sequencing statistics for T1 and T7 sections. B. Hematoxylin/Eosin staining of the T7 tumour section. C. Spatial plot showing spots clustered by regions (A-E). D. Spatial plots showing prediction scores in each spot for indicated cell signatures. E. Dual prediction using color-coded spots myCAF green, ccRCC/mes red, both cell types yellow. Supplementary Figure 9. Spatial transcriptomic localization of lymphoid populations. A-B. Projection of the indicated lymphoid cell populations on T1 and T7 tumour sections as described above. Supplementary Figure 10. Colocalization of ccRCC.mes and myCAF populations. A-C. Hematoxylin/Eosin staining of each indicated tumour section and projection of the indicated cell populations. Dual prediction using color-coded spots myCAF green, ccRCC/mes red, both cell types yellow. Supplementary Figure 11. NicheNet analyses of potential ccRCC.mes and myCAF interactions. A-B. CellPhoneDB predicted interactions with ligand on ccRCC.mes and receptor on myCAFs or vice versa, respectively. C. NicheNet predicted interactions inducing the myCAF program by ligands expressed in ccRCC.mes cells targeting receptors expressed by pericyte mesangial cells. D. NicheNet predicted interactions inducing the EMT program by ligands expressed in myCAF cells targeting receptors expressed by ccRCC.mes cells. Supplementary Figure 12. Deconvolution analysis of BIONIKK cohort. A. Heatmap with deconvolution results of all cell populations. B. Reduced heatmap for deconvolution results of the indicated populations displayed as column-scaled CIBERSORTx absolute scores. C. Sorted Spearman correlation coefficients between the indicated ccRCC populations and all other identified populations in scRNAseq. D. Kaplan-Meier curve for overall survival according to ccRCC.mes proportions. E. Kaplan-Meier curve for overall survival according to myCAF proportion in ccRCC.mes-high tumours.