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https://doi.org/10.1038/s41467-020-18916-5 OPEN Single-cell RNA sequencing highlights the role of inﬂammatory cancer-associated ﬁbroblasts in bladder urothelial carcinoma ✉ Zhaohui Chen1,4, Lijie Zhou1,2,4, Lilong Liu1,2, Yaxin Hou1,2, Ming Xiong1, Yu Yang3, Junyi Hu1,2 & ✉ Ke Chen 1,2

1234567890():,; Although substantial progress has been made in cancer biology and treatment, clinical outcomes of bladder carcinoma (BC) patients are still not satisfactory. The tumor microenvironment (TME) is a potential target. Here, by single-cell RNA sequencing on 8 BC tumor samples and 3 para tumor samples, we identify 19 different cell types in the BC microenvironment, indicating high intra-tumoral heterogeneity. We find that tumor cells down regulated MHC-II molecules, suggesting that the downregulated immunogenicity of cancer cells may contribute to the formation of an immunosuppressive microenvironment. We also find that monocytes undergo M2 polarization in the tumor region and differentiate. Fur- thermore, the LAMP3 + DC subgroup may be able to recruit regulatory T cells, potentially taking part in the formation of an immunosuppressive TME. Through correlation analysis using public datasets containing over 3000 BC samples, we identify a role for inflammatory cancer-associated fibroblasts (iCAFs) in tumor progression, which is significantly related to poor prognosis. Additionally, we characterize a regulatory network depending on iCAFs. These results could help elucidate the protumor mechanisms of iCAFs. Our results provide deep insight into cancer immunology and provide an essential resource for drug discovery in the future.

1 Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. 2 Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, China. 3 Department of Pathology and Laboratory Medicine, Indiana University ✉ School of Medicine, Indianapolis, IN, USA. 4These authors contributed equally: Zhaohui Chen, Lijie Zhou. email: [email protected]; shenke@hust. edu.cn

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ladder carcinoma (BC) is one of the most prevalent uro- genes were enriched in immune-related pathways, especially B Bgenital malignant diseases around the world, with cell-related pathways (Fig. 1e). Comparing to normal epithelial approximately 430,000 new cases diagnosed and over cells, cancer cells almost lost the ability to produce immunoglo- 165,000 deaths caused per year1. Although substantial progress bulin, and they expressed lower levels of MHC-II molecules, has been made in cancer biology and treatment, clinical outcomes which was validated by immunofluorescence (Fig. 1f, g). These of bladder carcinoma patients are still not satisfactory2. results indicated that bladder cancer cells might downregulate In the last decade, the tumor microenvironment (TME) has immunogenicity to escape immune detection. In addition, we been a popular area of cancer biology research in relation to noticed that cancer cells possessing more CNVs seemed to therapeutic targets for drug discovery. Notably, BC is one of the express higher levels of IGF2 (Supplementary Fig. 3B, C). In the least immune infiltrated cancers3, which may account for the TCGA BLCA cohort, a high level of IGF2 was significantly related poor response to anti-PD1 therapy. Since then, designing new to poor prognosis (Supplementary Fig. 3C). Pathway analysis treatment strategies for BC has continued to be an arduous task. with gene set variation analysis (GSVA) revealed that E2F targets, Previously, molecular subtypes of BC have been reported to MYC targets and the G2M checkpoint pathway were enriched in show different cell type-specific expression patterns4, which the CNV high group, while inflammatory and other immune- indicates that the heterogeneity of BC at least partly results from related pathways were downregulated (Fig. 1h). These results different cell type fractions inside the microenvironment. How- further confirmed that cancer cells in the advanced stage of BC ever, relevant studies have been rare until recently. downregulated immunogenicity and displayed high proliferation In the current study, we profile the transcriptome of ability. 52721 single cells from bladder urothelial carcinoma or paratumor mucosa samples and produce an atlas of the whole TME Monocytes recruited into the tumor region experience M2 inside bladder cancer tissues. Our work highlights the role of one polarization. Reclustering of myeloid cells identified seven cell cancer-associated fibroblast (CAF) subset, named inflammatory types: tumor-associated macrophages (TAMs, MRC1+C1high); cancer-associated fibroblasts (iCAFs), in BC and discovers pos- CD1C+ dendritic cells (CD1C+ DCs, CD1C+CLEC10A+); sible therapeutic targets for BC treatment. In addition, we monocytes (S100A8+S100A9+); proliferating myeloid cells investigate the relation between TME and molecular subtypes of (TOP2A+); cross-presenting DCs (CLEC9A+); follicular B cells BC by correlating scRNA-seq data to over 3500 bulk RNA (CD79A+MS4A1+); and LAMP3+ DCs (LAMP3+CCR7+). sequencing or microarray profiles in public datasets. These results These cell subgroups were confirmed by flow cytometry (Sup- promote the understanding of heterogeneity between patients and plementary Fig. 4A). Two cell clusters expressed both myeloid provide a basis for individualized treatment for bladder urothelial markers, and epithelial or endothelial markers, and they were carcinoma. considered doublets (Fig. 2a, Supplementary Fig. 5A–C). Differ- ences between cell types could also be identified by the activity of Results TF motifs (Supplementary Fig. 5D). Notably, monocytes mostly Single-cell sequencing and cell type identification. After quality originated from normal mucosal tissues, while TAMs were enri- control and removal of the batch effect between batches (Sup- ched in BC tissues. In addition, it seems that the transcriptomes plementary Fig. 1, see “Methods”), 52721 single cells were clus- of these two cell types exhibited continual changes (Fig. 2b, tered into eight major clusters. Cluster-specific genes were used to Supplementary Fig. 5B, red blank), indicating that monocytes annotate cell types with classic markers described in previous recruited into the tumor region were reprogrammed into TAMs, studies5: epithelial (EPCAM+) cells; endothelial (CD31+) cells; which has been previously reported in a murine breast cancer two types of fibroblasts (COL1A1+)—iCAFs (PDGFRA+) and model7. To further investigate this ongoing process, we per- myo-CAFs (mCAFs) (RGS5+); B cells (CD79A+); myeloid cells formed trajectory analysis and RNA velocity analysis on mono- (LYZ+); T cells (CD3D+); and mast cells (TPSAB1+) (Fig. 1a, b, cytes and TAMs8,9. Similar phenomenon was observed in both Supplementary Fig. 2A–D). two computational pipelines (Fig. 2c and Supplementary Fig. 6B). Combined with the key motifs identified by SCENIC, we recognized that the activity of three motifs, BACH1, MAFG, and NFE2, Cancer cells show high heterogeneity due to CNV patterns. was downregulated, while activation of the MAF, STAT1 and EPCAM+ epithelial cells (EPCs) were reclustered to produce 17 STAT2 motifs led to this M2-polarization process (Fig. 2c, d, clusters (Fig. 1c, d). Interestingly, although the batch effect has Supplementary Fig. 6A). These results provide potential targets been previously removed, cancer cells still showed a patient- for inhibiting or reversing the formation of the immunosup- specific expression pattern, which indicated extremely high het- pressive microenvironment. In addition, we noticed that the co- erogeneity that was probably caused by copy number variations inhibitors CD274, LGALS9, CD276, TIGIT, and PDCD1LG2 (CNVs)6; this assumption was confirmed by InferCNV (Fig. 1c, were all upregulated in this differentiation process, while co- Supplementary Fig. 3A). As shown in Supplementary Fig. 3A, activators were downregulated (Fig. 2e). CNVs accumulated in most high-grade tumor-derived EPCs and showed high heterogeneity among clusters. Despite the heterogeneity, almost all high-grade cancer cells possessed deletions LAMP3+ DCs recruit Tregs into the tumor region via cytokines. from chromosomes 9 and 11 and amplifications in chromosomes Among the 3 DC subgroups, LAMP3+ DCs expressed various 19 and 20. In addition, some cells from tumor tissue possessed genes encoding cytokines, including CCL17, CCL19, and CCL22 almost no CNV and showed a similar expression pattern to (Fig. 2f). Strikingly, these cytokines originated almost entirely from normal EPCs (Fig. 1d), which indicated that these cells may be LAMP3+ DCs in BC (Fig. 2g). As discussed in a previous study, non-malignant EPCs. Therefore, EPCs were divided into 4 groups CCL17 and CCL22 have strong chemotaxis towards Tregs via based on CNVs: low, high, control, and undetected in this binding to CCR4 expressed on the membrane of Tregs10,11.Inthe investigation. TCGA BLCA cohort, the LAMP3+ DC signature was highly When comparing the transcriptomes, we noticed that a series positively correlated with the Treg signature and Th2 signature, of genes was especially expressed in the control and CNV which were both CCR4+, but there was not a high correlation with undetected groups but almost absent in tumor cells (Fig. 1d, red the CTL signature (Fig. 2h). LAMP3+ DCs were significantly blank). Gene Ontology enrichment analysis revealed that these enriched in tumor tissues (Supplementary Fig. 5A). Additionally,

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a b Bioinformatic analysis 40 B cell High grade Myeloid Low grade Bladder mucosa Mast cell Normal 20 Primary tumor tissues iCAF Epithelial Dissociation High-throughput sequencing 0 tSNE2 1 Single cell suspension 2 Endothelial –20 3 10X genomics panel 4 mCAF 5 T cell 6 7 –40 8 –40 –20 0 20 40 tSNE1 c Cluster Patient f

HLA-DRA HLA-DRB1 CD74

Grade CNV g EPCAM MHC ll MERGR Normal UMAP2 UMAP1 d Tumor

Patient Grade CNV h Estrogen_response_early Hedgehog_signaling 100 cluster Estrogen_response_late 4 P53_pathway Cluster Apoptosis 2 0 Coagulation 0 0 Kras_signaling_up 1 –2 UV_response_up 2 –4 Interferon_gamma_response 3 Angiogenesis –100 Patient 4 Mitotic_spindle TGF_beta_signaling 1 5 Androgen_response 2 6 Hypoxia 3 7 Kras_signaling_dn Apical_surface 4 8 Heme_metabolism 5 9 Interferon_alpha_response 6 10 Pl3K_akt_mtor_signaling 7 11 WNT_beta_catenin_signaling Epithelial_mesenchymal_transition 8 12 Inflammatory_response Grade 13 Tnfa_signaling_via_nfkb 14 Complement High grade 15 IL6_jak_stat3_signaling Low grade IL2_stat5_signaling 16 Normal UV_response_dn Allograft_rejection CNV Apical_junction Myogenesis Low E2F_targets High Pancreas_beta_cells Not detected G2M_checkpoint Control MYC_targets_v2 Spermatogenesis Xenobiotic_metabolism Adipogenesis Bile_acid_metabolism e Peroxisome Humoral immune response Fatty_acid_metabolism –Log10(FDR) Oxidative_phosphorylation Leukocyte migration Dna_repair Regulation of B cell activation 10 Glycolysis Positive regulation of lymphocyte activation MYC_targets_v1 Notch_signaling Phagocytosis, recognition 9 Protein_secretion Phagocytosis, engulfment 8 Reactive_oxigen_species_pathway Plasma membrane invagination Unfolded_protein_response B cell mediated immunity 7 Cholesterol_homeostasis MTORC1_signaling Stress response to metal ion High Low Not detected Control 0.0 2.5 5.0 7.5 10.0 12.5 Num of genes

Fig. 1 Identifying infiltrated cell types in BC and non-malignant tissues. a, b Identifying infiltrated cell types in BC and non-malignant tissues. a Workflow of the sample preparation, sequencing and bioinformatic analysis. b tSNE plot of single cells profiled in the presenting work colored by major cell types, tumor grade and patient. c–h Reclustering of EPCAM+ cells. c UMAP plot EPCAM+ cells (epithelial marker) colored by cluster, patient, grade and CNV level. d Heatmap of differentially expressed genes (DEGs) of every CNV group. e Enriched GO functions of downregulated genes in malignant cells. f Expression levels of MHC-II molecules and CD74. g Immunofluorescence (IF) staining of MHC-II molecules and EPCAM. Scale bar represents 50 μm. h Heatmap shows difference in pathway activities scored by GSVA per cell between different CNV groups. Shown are t-values from a lineal model.

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a bd

Celltype 4 2 patient 0 BACH1 MAF Origin –2 cluster –4 TAM C1QB C1QC CD1C+ DC APOE Celltype Monocyte TAM Proliferation myeloid Monocyte Patient Cross-presenting DC 1 C1QA Doublets (Epithelial) 2 APOC1 3 B cell SLC40A1 4 TREM2 5 MAFG STAT1 LAMP3+ DC 6 Doublets (Endothelial) 7 8 Origin MSR1 Normal

tSNE2 CXCL8 Tumor CCL3L3 Cluster tSNE1 CXCL3 0 CCL4L2 1 CCL3 3 lL1B 5 CCL4 S100A8 9 c CXCL2 NFE2 STAT2 CCL20 S100A9

1 Pseudotime e CD44 0510 TNFSF8 C10orf54 Motif AUC Cluster TNFRSF8 TNFSF14 Cluster 0 TNFSF9 1 Pseudotime

1 1 GEM 2 Cluster 3 h CD7 3 3.0 COR = 0.241 5 0 5 CD27 P < 0.001 2 1 9 9 TIGlT

Component 2 3 2.0 LAG3 1 CD38 Component 1 0 CD274 1.0 LGALS9 –1 Celltype HAVCR2 f Celltype CD1C+ DC patient PDCD1LG2 –2 CTL (log2TPM+1) Origin Cross–presenting DC 0.0 LAMP3+ DC CD276 cluster –3 patient CD86 1.0 FCER1A 1 COR = 0.689 S100A4 2 CD40 FCER1G 3 P < 0.001 CLEC10A 4 0.6 ALOX5AP 5 CD1C 6 7 g 6 8 0.4 4 Origin CCL17 C1orf54 Normal 2 DNASE1L3 0.2 Tumor 0 CLEC9A Cluster

lDO1 6 Th2 (log2TPM+1) LGALS2 2 6 0.0 CST3 4 7 CCL19 lRF8 2 11 CCL19 0 1.5 CCL17 COR = 0.711 CCL22 4 4 TXN 3 P < 0.001 BIRC3 2 CCL22 2 FSCN1 0 1 1.0 CSTA 0 –2 LAMP3 –4 2.0 1.5 CD274 1.0 0.5 0.5 0.0

CCR7 (log2TPM+1) Treg iCAF TAMTreg 0.0 B cell mCAF NKT cell Epithelial Mast cellMonocyte CD1C+ DC Endothelial CD4+CD8+ T cell T cell LAMP3+MALT DC B cell Plasma cell 0 246 Naive/CM T cell

Doublets (Epithelial) Proliferation myeloid LAMP3+ DC (log2TPM+1) Cross–PresentingDoublets (Endothelial) DC

Fig. 2 Reclustering of myeloid-derived cells (LYZ+). a tSNE plot of subgroups of LYZ+ single cells. b DEGs between monocytes and TAMs. Single cells in red blank show features of both groups. c Trajectory of differentiation from monocyte into TAMs predicted by monocle 2. d Signiﬁcantly inhibited or activated TF motifs in the differentiation process colored by cell clusters. e Heatmap show upregulated or downregulated immune checkpoints in the differentiation process. f Heatmap of DEGs between three different DC subgroups. g Violin plot show expression level of cytokines and CD274 highly expressed in LAMP3+ DCs. h Correlation between LAMP3+ DCs and different T cell subgroups in TCGA BLCA cohort. Coefﬁcient was calculated with spearman correlation analysis.

LAMP3+ DCs showed the highest level of CD274 (Fig. 2f), which CXCL1, and CXCL2, which is similar to iCAFs described by was even higher than what was observed in Tregs from BC tissues, Öhlund D et al. in a pancreatic cancer model12; RGS5+ fibro- indicating that this DC subgroup could inhibit CD8+ Tcells blasts have characteristics that are similar to myo-cancer- directly or via recruiting Tregs into the tumor region. SCENIC associated fibroblasts (mCAFs) (Fig. 3a, b). Existing iCAFs and analysis revealed that RELB and KDM2B motifs were highly mCAFs were assessed in tumor and non-malignant bladder activated in LAMP3+ DCs (Supplementary Fig. 5E–G). stroma tissue by immunofluorescence (Fig. 3c). The results demonstrated that CAFs possess analogous subgroups across cancer types12,13. Two different fibroblast subtypes are identified in BC. Fibro- To investigate the function of each subgroup, we performed blasts (COL1A1+) were clustered into two different types: GO enrichment analysis on the DEGs of iCAFs and mCAFs. As PDGFRA+ fibroblasts exhibit strong expression of various shown in Fig. 3d, iCAFs were related to extracellular matrix cytokines and chemokines, including CXCL12, IL6, CXCL14, organization, regulation of cell migration, and angiogenesis,

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ab4 e Patient 2 Degradation of extracellular matrix 6 Grade 0 cluster –2 NES = 1.802 –4 0.6 celltype Pval = 0.000 FDR = 0.000 7 PTGDS Patient 0.4 2 LUM 1 CFD 2 FBLN1 0.2 0 APOD 3 1 DCN 4 CXCL14 5 0.0 SFRP2 MMP2 6 4 7 8 3 Grade High grade Cytokin–cytokine receptor interaction Low grade PLA2G2A Normal 0.6 RGS5 NES = 1.817 NDUFA4L2 Cluster ADIRF 0 0.4 Pval = 0.000 CRIP1 1 Enrichment score (ES) FDR = 0.000 mCAF iCAF PPP1R14A MYL9 2 0.2 MYH11 3 5 6 0.0 7 Celltype MT1M iCAF CCL21 RGS5 PDGFRA CCL19 mCAF iCAF mCAF

c f Muscle contraction MERGE RGS5 PDGFRA 0.0 –0.2 –0.4 NES = –1.848

Tumor –0.6 Pval = 0.000 FDR = 0.000 –0.8

MERGE RGS5 PDGFRA PGCIA pathway

0.0

Normal –0.2 Enrichment score (ES) –0.4 NES = –1.703 Pval = 0.000 –0.6 FDR = 0.000

d –logFDR Extracellular structure organization Extracellular matrix organization 20 iCAF mCAF Receptor ligand activity Negative regulation of locomotion 15 Negative regulation of cell migration Regulation of vasculature development 10 Regulation of cell—substrate adhesion g Regulation of angiogenesis 5 4 Growth factor activity CXCL12 Myeloid leukocyte migration iCAF enriched 3 0 10 20 30 2 Muscle system process 1

Muscle contraction 6 Expression level Focal adhesion 0 Actin binding 5 Extracellular matrix B cell Endothelial Epithelial iCAF Mast cell mCAF Myeloid T cell Collagen—containing extracellular matrix Urogenital system development 4 Negative regulation of cell migration j CXCL12 PDGFRA DAPI Myofibril Stress fiber mCAF enriched 0 51015 Number of genes

hi Tumor 15 1.00 COR = 0.684 High (n=201) P=0.000 Low (n=201) 0.75 10 0.50

5 0.25 p = 0.0486 Normal Survival Probability CXCL12 (log2TPM) 0 5 10 0 40 80 120 160 CD163 (log2TPM+1) Time (months)

Fig. 3 Fibroblasts in BC could be divided into two different subgroups. a tSNE plot of fibroblasts colored by clusters (up) and subgroup markers (down). b Heatmap of DEGs between different fibroblast subgroups. c IF confirmed the existence of iCAFs and mCAFs (n = 30). Scale bar represents 50 μm. d Enriched GO functions of upregulated genes in iCAFs and mCAFs. e, f GSEA shows top enriched pathways in iCAFs (3E) and mCAFs (3F). NES denotes normalized enrichment score. g Violin plot shows expression level of CXCL12 across major cell types. h Correlation between CXCL12 level and tumor- infiltrated macrophages in TCGA BLCA cohort. Coefficient was calculated with spearman correlation analysis. i High level of CXCL12 predicted poor prognosis in TCGA BLCA cohort. Log-rank p value < 0.05 was considered as statistically significant. j IF recognized CXCL12+ iCAFs in BC tissues. iCAFs are the major derivation of CXCL12 in tumor tissues. Scale bar represents 50 μm.

NATURE COMMUNICATIONS | (2020) 11:5077 | https://doi.org/10.1038/s41467-020-18916-5 | www.nature.com/naturecommunications 5 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18916-5 whereas the muscle system process, focal adhesion, and bladder mucosa mainly contains EPCs, while fibroblasts are extracellular matrix-associated pathways were significantly mainly located in stromal tissues, this phenomenon is probably a enriched in mCAFs. GSEA similarly revealed that iCAFs were result of sampling depth. This could obscure the identification of associated with extracellular matrix degradation, indicating a real DEGs between normal epithelial and tumor cells (Supple- potential role in tumor metastasis. The cytokine–cytokine mentary Fig. 7D, E). receptor interaction pathway was also enriched in iCAFs. In Subsequently, we expanded the clinical cohort, collecting over contrast, muscle contraction and the PGC1A pathway were 3000 microarray profiled BC and non-malignant mucosa samples enriched in mCAFs, corresponding to a previous in vitro12 study from the GEO and ArrayExpress databases (see “Methods”). A (Fig. 3e, f). possible batch effect was eliminated with Combat function, and Since the cytokine–cytokine receptor interaction was enriched then 2959 tumor samples were clustered into five major groups by in iCAFs, we investigated the expression level of cytokines in the ConsensusClusterPlus19 (Supplementary Fig. 8A–C). Four major BC TME. Dramatically, iCAF was the major source of CXCL12, clusters revealed similar characteristics to the luminal-papillary, which is related to the accumulation of TAMs via CXCL12/ luminal, luminal-infiltrated, and basal-squamous clusters. CXCR4 interactions14. Notably, CXCL12 was positively corre- Another cluster, which was unlike Neural group in TCGA BLCA, lated with the TAM signature in the TCGA BLCA cohort. A showed both luminal-squamous and Luminal characteristics, so it higher level of CXCL12 was significantly associated with a poor was named as Luminal transition. As shown in Fig. 5e–h, OS in prognosis. Immunofluorescence staining confirmed that CXCL12 this meta cohort highly corresponded to that of TCGA, was expressed by iCAFs in BC tissues (Fig. 3g–j). demonstrating the important role of CAFs in BC progression. Via SCENIC analysis, we identified essential motifs in both iCAF and mCAF have many similar features, which may make CAF subgroups. MEF2D and MEF2C are mCAF-specific motifs the result of CIBERSORTx insufficient. In the cohort from that have profound roles in the transcriptional regulation of TCGA, the iCAF-specific marker PDGFRA was significantly muscle lineages15. TCF21 and TWIST2 motifs were highly related to poor OS in BC patients, while the mCAF marker RGS5 activated in iCAFs (Fig. 4a, b). In a previous study, TCF21 was was not, indicating that iCAF may have a more important role found to be associated with coronary heart disease, enhancing the than mCAF (Supplementary Fig. 7F). “fibromyocyte” phenotype of smooth muscle cells16. TWIST2 is a driver of epithelial–mechanism transition (EMT). However, their Constructing an iCAF-based regulatory network for BC. Using roles in CAF are still unknown. CellphoneDB220, we investigated the cell–cell interaction network among the cell types identified in our present work. Notably, iCAFs have a pro-proliferation effect in BC. iCAFs were pre- iCAFs showed the most interactions with other cell types, and dicted to have growth factor activity by GO enrichment. To they showed especially strong interactions with ECs (Fig. 6a). confirm this hypothesis, we analyzed the expression levels of Considering the results of GO analysis and GSEA and the VEGF, FGF and IGF families in the BC TME (Fig. 4c). Drama- expression abundance in our data, we collected data on interac- tically, iCAFs are the major source of various growth factors. tion pairs including growth factors, CCL and CXCL families Among these growth factors, IGF1, an iCAF-specific group factor, (Supplementary Fig. 9A). was related to poor overall survival (Fig. 4d, e). iCAFs express higher levels of CXCL12, the receptors of which To validate its pro-proliferation effect, we sorted iCAFs by flow includes DPP4, CXCR3, CXCR4, and ACKR3 (CXCR7). As cytometry and co-cultured them with bladder urothelial carci- CXCR4 and CXCR3 are widely expressed on immune cells, noma cell lines in vitro. Significantly, the co-culture group secretion of CXCL12 by iCAFs is responsible for the immune showed higher proliferation ability, which confirmed the infiltration status of BC. CCL2 and CXCL1 secreted by iCAFs protumor role of iCAFs in BC (Fig. 4f, g). could interact with ACKR1, which is highly expressed on the surface of ECs. These cytokines were previously reported to be associated with metastasis of BC21,22, and we pinpointed their Correlating scRNA-seq to public datasets. To investigate the origin to iCAFs (Fig. 6b, d). clinical role of the cell types identified in the present study, we We also suggested that iCAFs produce VEGF, including evaluated the fraction of every cell type in samples from the VEGFA and VEGFB, which bind to VEGF receptors (FLT1, KDR, TCGA BLCA cohort with CIBERSORTx17 (Supplementary MET, and FLT4) on ECs to promote angiogenesis. In addition, Fig. 7A–C, see “Methods”). Strikingly, only the accumulation of FGFR1 was expressed on iCAFs and ECs, while FGFR3 was iCAFs and mCAFs was related to poorer overall survival (OS) expressed on tumor cells. These receptors could bind to FGF, (Fig. 5a, b). When correlating the cell fraction data with clinical including FGF2 and FGF7 derived from iCAFs, and show pro- information, we noticed that the cell type abundance was altered proliferation effects. IGF1R, a receptor for IGF1, was expressed greatly across the molecular subtypes described in a previous on tumor cells and stroma cells, which suggested that iCAF could study4 (Fig. 5c, d). Luminal papillary, with the highest tumor also factor into resistance to cisplatin23. Notably, tumor cells purity, had the best prognosis, while the OS of the other four express high levels of VEGFA, which has the strongest groups did not show a significant difference. Basal squamous proangiogenic ability. In addition, high-grade tumor cells highly showed the lowest tumor purity, as almost all cell types identified express IGF2, interact with IGF2R on iCAFs, and promote tumor in our work occurred in this group. Notably, T cells were also progression24 (Fig. 6c, e). Together, our results predicted that enriched in this group, indicating that anti-immune checkpoint iCAFs could promote the proliferation of tumor cells and stromal therapy may be suitable for these patients. In contrast to other cell cells and potentially be able to recruit immune cells into the types, mCAFs and iCAFs were enriched in four groups, with the tumor stage. exception of the luminal-papillary group. Since the luminal- papillary group contained mostly early stage samples, these results demonstrated that CAFs were closely related to the tumor Discussion progression of BC. In addition, we noticed that fibroblast markers Treatment of bladder urothelial carcinoma, especially muscle were significantly downregulated in tumor tissues, while epithelial invasive bladder urothelial carcinoma is still a big problem until markers were upregulated. This could explain why downregulated now. Anti-immune checkpoint therapy, including PD1/PD-L1, DEGs were enriched in the extracellular region18. Since normal benefits only nearly 30% of patients with advanced disease.

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a b Patient grade 4 2 MEF2C MEF2D TCF21 TWIST2 celltype 0 SALL1 (+) –2 –4 TCF21(+) patient 1 NFE2L2(+) 2 3 TWIST2(+) 4 5 CREB3L1(+) 6 MEF2C targets MEF2D targets TCF21 targets TWIST2 targets 7 MEF2D(+) 8 Grade MAFK(+) High grade Low grade MEF2C(+) Normal celltype TBX2(+) iCAF NR2F2(+) mCAF c T cell Per exp Myeloid 0 20 mCAF 40 60 Mast cell iCAF Ave exp Epithelial 2 1 Endothelial 0 B cell –1 MET HGF KDR lGF2 lGF1 FLT4 FLT1 FGF7 FGF9 FGF6 FGF8 FGF3 FGF4 FGF1 FGF2 FGF5 lGF2R lGF1R FGF11 FGF21 FGF22 FGF14 FGF23 FGF19 FGF17 FGF20 FGF13 FGF16 FGF18 FGF12 FGF10 FGFR3 FGFR2 FGFR1 FGFR4 VEGFA VEGFB VEGFC Growth factor Receptor e d 1.00 IGF1 High (n=201) Low (n=201) f 0.75 250K 250K

0.50 200K 200K 95.3% 0.25 150K 150K p = 0.0006 FSC-A FSC-H Survival probability 0 100K 98.4% 100K 04080 120 160 50K 50K Time (months)

0 0 3 3 4 5 g 2.5 0 50K 100K 150K 200K 250K –10 0 10 10 10 Ctrl iCAF FSC-A CD31-BB700 2.0

1.5 250K 250K 1.0 T24 200K 200K 0.5

0.0 150K 150K 3 60.9% SSC-A 100K FSC-H 100K Relative area 2 81.2% EJ 50K 50K 1 0 0 100 101 102 103 104 105 –103 0 103 104 105 0 PDGFRA-BV421 CD45-FITC

Ctrl iCAF

Fig. 4 iCAFs promote proliferation of cancer cells. a Heatmap of the area under the curve (AUC) scores of TF motifs estimated per cell by SCENIC. Shown are top ﬁve differentially activated motifs in iCAFs and mCAFs, respectively. b tSNE plots of the expression levels of TFs (up) and AUC scores (down). c Dot plot shows the expression level of growth factors across cell types. iCAFs are the major producer of growth factors. d tSNE plot shown the expression level of IGF1. IGF1 is secreted almost only by iCAFs. e High level IGF1 represents poor overall survival in TCGA BLCA cohort. P value was calculated with log-rank test. f FACS sorting strategy of iCAFs. g Co-culture and colony formation experiment showed that iCAFs have pro-proliferation property in vitro (n = 5). Error bar: mean value ± sd. P values were determined by two-side Student’s t test. ***p < 0.001.

New targets or combined therapy strategies are still waiting to In the present study, we identiﬁed 19 different cell types in the be discovered, and such discoveries could be hastened by a BC microenvironment. We suggest that the downregulated better understanding of the TME of bladder urothelial carci- immunogenicity of cancer cells potentially contributes to the noma. Here, we generated a single-cell transcriptome atlas and formation of an immunosuppressive microenvironment. We revealed the components of the microenvironment inside BC found that LAMP3+ DCs were related to the recruitment of tissues. Tregs and other CCR4+ immune cells. Since blocking of CCR4

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TCGA BLCA cohort a b c p = 0.047 1.00 iCAF high mRNA cluster 4 CD1C+ DC 2 iCAF low Histologic subtype 0 Epithelial 0.75 n(high)=197 Grade –2 n(low)=197 SNV –4 CD8 0.50 Stage mRNA cluster B cell Epithelial Basal_squamous Luminal Cross–presenting DC 0.25 Endothelial Luminal_infiltrated p = 0.015 Luminal_papillary Mast cell Treg Neuronal 0 Histologic subtype LAMP3 0 40 80 120 160 ND Endothelial Non–papillary Monocyte Papillary LAMP3+ DC 1.00 mCAF high CD1C Grade mCAF low High grade TAM Survival probability 0.75 B Low grade n(high)=197 ND Treg n(low)=197 Cross SNV 0.50 High Monocyte TAM Low p = 0.015 CD8 Media iCAF 0.25 Stage p = 0.002 mCAF l mCAF p = 0.002 ll 0 iCAF lll lV 0.5 1.0 1.5 2.0 2.5 04080 120 160 Mast ND HR and 95% CI Time (months) d ef 1.00 Basal_squamous 1.00 Basal_squamous Luminal Luminal Luminal–transition Luminal_infiltrated 1.00 1.00 Luminal_infiltrated 0.75 Luminal_papillary mCAF high iCAF high 0.75 Luminal_pappilary Neuronal mCAF low iCAF low 0.75 n(high)=370 0.75 n(high)=370 0.50 n(low)=370 n(low)=370 0.50 0.50 0.50 0.25 0.25 0.25 0.25 p < 0.0001 p = 0.0008 p = 0.025 p = 0.011 0.00 0 0 0.00 0 40 80 120 160 0 50 100 150 0 50 100 150 0 50 100 150 Time (months) GEO meta-cohort gh CD1C+ DC 4 mRNA.cluster p = 0.005 2 Epithelial T.stage 0 p = 0.032 grade –2 B cell N.stage –4 mRNA. cluster M.stage Basal_squamous Cross–presenting DC Luminal Monocyte Luminal–transition LAMP3+ DC Luminal_infiltrated mCAF Luminal_pappilary Endothelial iCAF Not defined T.stage Mast CIS CD8 Not detected CD1C T2-T4 Mast cell Ta-T1 LAMP3 Tx TAM Grade TAM Gx High grade Treg CD8 Low grade p = 0.025 Cross Not detected mCAF N.stage p = 0.011 B N0 iCAF N1 p = 0.001 Treg Not detected Monocyte M.stage Endothelial M0 Epithelial M1 0.5 1.0 1.5 2.0 Not detected HR and 95% CI

Fig. 5 Molecular subtypes of BC were caused by heterogeneity of TME. a Association between relative cell abundance and patient survival from TCGA BLCA cohort (COX regression analysis). b Kaplan–Meier curves for TCGA BLCA patients. P value was calculated with log-rank test. c Heatmap of cell abundance predicted per sample from TCGA BLCA cohort by CIBERSORTx. Shown are row z-score. d Kaplan–Meier survival curve for TCGA BLCA patients, grouped by molecular subtypes. e Kaplan–Meier survival curves for microarray-based meta-cohort patients. f Kaplan–Meier survival curve for microarray-based meta-cohort patients, grouped by molecular subtypes. P values of (d–f) were calculated with log-rank test. g Association between relative cell abundance and patient survival from microarray-based meta-cohort (COX regression). h Heatmap of cell abundance predicted per sample from microarray-based meta-cohort by CIBERSORTx. Shown are row z-score. could signiﬁcantly reduce Tregs recruited in the tumor stage in a By correlating our results with those from public databases, we canine model, LAMP3+ DCs may also be a potential target for demonstrated that CAFs, especially one subset of them, iCAFs, immune therapy25. Furthermore, we also suggested that stromal were the key factor in tumor progression in BC. At present, cells, especially iCAFs, have strong pro-proliferation properties, immune therapy mainly targets T cells (PD1/PD-L1, CTLA-4)26 which has rarely been discussed in BC models. We also produced or TAMs (CSF1R)27. However, these immune cells almost do not a regulatory network based on iCAFs in a bioinformatic way. exist in a substantial number of BC patients, which may cause These results will help elucidate the role of stomal cells in BC. In there to be no response for such patients to immune checkpoint the future, more function assay may help further understand the inhibition therapy. Since CAFs were detected in almost all BC underline mechanism. patients in an advanced stage, and since they were the major

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ac Naive T cell VEGFB NRP1 CLEC9A+ DC VEGFB FLT1 complex Treg 200 VEGFB FLT1 CD4+ T cell CD8+ T cell VEGFB ADRB2 NKT cell 150 VEGFA NRP2 Mast cell B cell VEGFA NRP1 100 LAMP3+ DC VEGFA KDR mCAF TAM VEGFA FLT1 complex 50 Epithelial VEGFA FLT1 CD1C+ DC –log10(P value) PGF FLT1 Monocyte 0 Endothelial PDGFC FLT4 1 iCAF

Naive T cell Naive T CLEC9A+ DC Treg cell CD4+ T cell CD8+ T NKT cell Mast cell B cell LAMP3+ DC mCAF TAM Epithelial CD1C+ DC Monocyte Endothelial iCAF lGF2 lGF2R 2 lGF2 lGF1R 3 lGF1 lGF1R lGF1 a6b4 complex HGF MET HGF CD44 b FGF9 FGFR4 CXCL9 DPP4 2 CXCL8 ACKR1 FGF9 FGFR3 1 CXCL2 DPP4 FGF9 FGFR2 0 CXCL16 CXCR6 FGF9 FGFR1 –1 CXCL12 DPP4 –log10(P value) FGF7 FGFR4 –2 CXCL12 CXCR4 0 CXCL12 CXCR3 1 FGF7 FGFR3 CXCL12 ACKR3 2 FGF7 FGFR2 CXCL10 DPP4 log2(mean(ligand, receptor)) 3 FGF7 FGFR1 CXCL1 ACKR1 CCL5 CCR5 FGF2 FGFR4 CCL5 CCR1 FGF2 FGFR3 4 CCL5 ACKR1 FGF2 FGFR2 2 CCL4L2 PGRMC2 FGF2 FGFR1 CCL22 DPP4 0 CCL21 CCR7 FGF2 CD44 CCL2 CCR10 –2 FGF10 FGFR2 CCL2 ACKR1 FGF10 FGFR1 CCL17 ACKR1 log2(mean(ligand, receptor)) CCL13 CCR1 iCAF TAM

CCL11 DPP4 iCAF mCAF TAM NKT cell Mast cell B cell iCAF Epithelial mCAF Endothelial CD1C+ DC Treg TAM Mast cell LAMP3+ DC iCAF Epithelial B cell Monocyte CLEC9A+ DC mCAF Endothelial CD1C+ DC TAM CD4+ T cell NKT cell Mast cell Epithelial mCAF Monocyte CLEC9A+ DC

Endothelial iCAF CD1C+ DC NKT cell Naive T Naive cell CD8+ T cell CD4+ T cell Mast cell Epithelial LAMP3+ DC Monocyte CD1C DC CD4 T cell CLEC9A+ DC Endothelial CD8+ T cell LAMP3+DC iCAF CLEC9A DC

deEndothelial

CD8+ CD4+ Treg B cell

Naive T cell

VEGFA

mCAF Cancer cell

mCAF

CXCL12 CCL11 FGF7 VEGF IGF2 FGF CXCR3 CCL2 VEGFR CXCL12 iCAF CXCR4 CXCL1 CLEC9A+ DC IGF1R ACKR3 FGFR ACKR1 lGF1 IGF2R CCR10

DPP4 iCAF Endothelial

Fig. 6 Cell–cell communication network in BC TME. a Heatmap show number of potential ligand-receptor pairs between cell groups predicted by CellphoneDB 2. b, c Bubble plots show ligand-receptor pairs of cytokines b and growth factors c between iCAFs and other cell groups. d, e Predicted regulatory network centered on iCAFs. source of various validated protumor growth factors in the TME, then, batch derived difference was removed with Seurat V3 in targeting CAFs may be an optimal choice for BC treatment. this study. Although we highlighted the role of iCAFs in this pipeline In summary, we identified the expression profiles of subsets of instead of mCAFs, the role of mCAFs in progression of cancer cell in bladder urothelial carcinoma and confirmed the char- still could not be excluded. Limited by the deconvoluting algo- acteristics of these tumor-associated subsets. This cell atlas pro- rithm, it’s still difficult to identify accurate proportion of iCAFs vides deep insight into cancer immunology and is an essential and mCAFs in bulk sequencing data, which have highly similar resource for drug discovery in the future. features in transcriptome. More proofs are needed to be validate the role of mCAFs in BC. Methods Since fresh samples are needed in current single-cell sequen- Patients and samples. All samples were obtained from the Union Hospital of cing strategy, batch effect could be involved between samples Tongji Medical College, Huazhong University of Science and Technology, Wuhan, loaded in batches, which is still a big problem in bioinformatic China. Eight primary bladder tumor tissues (2 low-grade bladder urothelial tumors, analysis of single-cell sequencing data. Previously, Tran et al. has 6 high-grade bladder urothelial tumors) along with 3 adjacent normal mucosae, systemically compared the efficacy of the most prevalent tools for were involved in this cohort. Patients provided informed consent for this work. All fi 28 experimental procedures were approved by the Institutional Review Board of eliminating batch effects of single-cell pro ling data . Hence, Tongji Medical College, Huazhong University of Science and Technology Seurat V3 and Harmony are the most efficient tools. Since (IRB: S116).

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Single-cell suspension preparation. Bladder tumors and adjacent normal mucosa identification of different cell states from scRNA-seq data. To measure the dif- were processed immediately after being obtained from bladder cancer patients. ference between cell clusters based on transcription factors or their target genes, Every sample was cut into small pieces (<1 mm in diameter) and then was incu- SCENIC was performed on all single cells, and the preferentially expressed reg- bated with 2 ml of trypsin (Gibco, Cat: R001100), 1 ml of collagenase II (Biofrox, ulons were calculated by the Limma package31. Only regulons significantly upre- Cat: 2275MG100) and 100 µl of DNase (Servicebio, Cat: 1121MG010) for 1 h on a gulated or downregulated in at least one cluster, with adj. p-value < 0.05, were 37 °C shaker. Subsequently, 4 ml DMEM was added to dilute the suspension, and involved in further analysis. then a 40-μm cell mesh was used to filter the suspension. After centrifugation at 250g for 5 min, the supernatant was discarded, and then the cells were washed with – PBS twice. Then, the cell pellet was resuspended in 1 mL of ice cold red blood cell Cell cell communication analysis with CellPhoneDB 2. CellPhoneDB 2 is a lysis buffer and were incubated at 4 °C for 10 min. Next, 10 ml of ice-cold PBS was Python-based computational analysis tool developed by Roser Vento-Tormo et al added to the tube, and it was then centrifuged at 250g for 10 min. After decanting 20, which enables analysis of cell–cell communication at the molecular level. A the supernatant, the pellet was resuspended in 5 ml of calcium- and magnesium- website version was also provided for analysis of a relatively small dataset (http:// free PBS containing 0.04% weight/volume BSA. Finally, 10 µl of suspension was www.cellphonedb.org/). As described above, 52721 single cells that were clustered counted under an inverted microscope with a hemocytometer. Trypan blue was into 19 cell types were investigated using the software to determine interaction used to quantify live cells. networks. Interaction pairs whose ligands belong to the VEGF, FGF, CCL, or CXCL families and have P-values < 0.05 returned by CellPhoneDB, were selected for the evaluation of relationships between cell types. Droplet-based single-cell sequencing. According to the manufacturer’s protocol, Chromium Single cell 3′ Reagent v3 kits were used to prepare barcoded scRNA-seq libraries. Single-cell suspensions were loaded onto a Chromium Single-Cell Con- Correlation to public datasets. Transcriptome data from The Cancer Genome troller Instrument (10 × Genomics) to generate single-cell gel beads in emulsions Atlas (TCGA) BLCA datasets were obtained from UCSC XENA (https://xena.ucsc. (GEMs). To capture 8000 cells per library, approximately 12,000 cells were added edu/). Clinical information was presented by Robertson et al.4 in their supple- to each channel. After generation of GEMs, reverse transcription reactions were mentary materials. engaged to generate barcoded full-length cDNA, which was followed by disruption For cell subgroups, genes with FC >2 were considered as marker genes, Mean of emulsions using the recovery agent, and then cDNA clean-up was performed TPM level of marker genes were log2 transformed and used as gene signature. with DynaBeads Myone Silane Beads (Thermo Fisher Scientific). Next, cDNA was Spearman correlation analysis was used to estimate correlation between specific amplified by PCR for the appropriate number of cycles, which depended on the cell types. number of recovered cells. Subsequently, the amplified cDNA was fragmented, A microarray-based study of bladder urothelial carcinoma containing over 15 end-repaired, A-tailed, and ligated to an index adaptor, and then the library was tumor samples in the GEO or ArrayExpress databases was downloaded and amplified. Every library was sequenced on a HiSeq X Ten platform (Illumina), and normalized into a meta-cohort. Batch effects between cohorts based on the same 150 bp paired-end reads were generated. platform were initially removed by the Combat function of the sva package32. Subsequently, batch effects between different platforms were also removed. 3D fi fi 19 Raw data processing and quality control. Cell Ranger (version 2.2.0) was used to PCA was performed to con rm the ef ciency of Combat. ConsensuClusterPlus was used to detect major molecular clusters in the meta-cohort. process the raw data, demultiplex cellular barcodes, map reads to the tran- fi scriptome, and down-sample reads (as required to generate normalized aggregate To evaluate the relative abundance of each cell type identi ed in the present fi study, cibersortx17 was performed with default parameters. Subsequently, the data across samples). These process produced a raw unique molecular identi er – (UMI) count matrix, which was converted into a Seurat object by the R package relative cell abundance was divided into high 50% and low 50%. Kaplan Meier Seurat29 (version 3.0.0). Cells with UMI numbers <1000 or with over 10% analysis was performed to evaluate the prognostic value of cell clusters and detect mitochondrial-derived UMI counts were considered low-quality cells and were the role that these cell clusters play in bladder cancer progression. All these removed. In order to eliminate potential doublets, single cells with over 6000 genes analyses were performed in R (3.6.0). detected were also filtered out. Finally, 52721 single cells remained, and they were applied in downstream analyses. Pathway analysis. Differentially expressed genes (DEGs) of cell subgroups were After quality control, the UMI count matrix was log normalized. Since sample recognized by the findmarker function provided by Seurat. |FC| > 2 and adj.p.val < from eight patients were processed and sequenced in batches, patient number was 0.05 were used as the cut-off criteria. GO enrichment analysis was performed on used to remove potential batch effect. In this process, top 3000 variable genes were these DEGs with clusterProfiler33. GSEA was performed on a matrix of all genes used to create potential Anchors with FindIntegrationAnchors function of Seurat. detected by the desktop tool downloaded from http://software.broadinstitute.org/ Subsequently, IntegrateData function was used to integrate data and create a new gsea/index.jsp. GSVA was conducted with the GSVA package34. Differences matrix with 3000 features, in which potential batch effect was regressed out. between different cell groups were calculated with a linear model offered by the To reduce the dimensionality of the scRNA-Seq dataset, principal component Limma package. analysis (PCA) was performed on an integrated data matrix. With Elbowplot function of Seurat, top 30 PCs were used to perform the downstream analysis. The main cell clusters were identified with the FindClusters function offered by Seurat, Immunofluorescence staining. A tissue chip, HBla-U060CS-01, which consists of with resolution set as default (res = 0.8). And then they were visualized with 2D 30 paired tumor and paratumor samples, was purchased from Shanghai Outdo tSNE or UMAP plots. Conventional markers described in a previous study were Biotech. To ensure the consistency of the analysis, all immunofluorescence analyses used to categorize every cell into a known biological cell type. Firstly, 52721 cells were performed using the same type of tissue chip. were clustered into seven major cell types. Subsequently, every major cell type was The following antibodies were used to detect specific proteins: anti-HLA-DRA subset and further clustered into subclusters to detect heterogeneity within every (rabbit, 1:50, Proteintech, Cat. No. 11221-1-AP), anti-EPCAM (rabbit, 1:50, cell type, respectively. The Seurat Findallmarker function was performed to identify Proteintech, Cat. No. 21050-1-AP), anti-RGS5 (rabbit, 1:50, Proteintech, Cat. No. preferentially expressed genes in clusters or differentially expressed genes between 11590-1-AP), anti-PDGFRA (rabbit, 1:200, Abcam, ab203491), and anti-CXCL12 tumor- and normal-derived cells. Rds of total dataset, myeloid lineage and (rabbit, 1:100, Abcam, ab155090). fibroblast have been uploaded as Supplementary Software 1, which could be read in R environment by Shiny R package. Flow cytometry experiments. Tissue samples were disassociated as described above. CD31-CD45lowPDGFRA+ iCAFs were collected by flow cytometry. The Estimation of CNVs in cancer cells. The InferCNV package was used to detect the following antibodies were used: anti-CD31 (mouse, 1:300, BD, 566563), anti-CD45 + CNVs in EPCAM cells and to recognize real cancer cells with default parameters. (mouse, 1:100, BD, 555482), and anti-PDGFRA (mouse, 1:500, BD, 562799). Anti- Two clusters mainly containing non-malignant derived cells were used as the CD45 (mouse, 1:100, BD, 555482), anti-CD3 (BD, 1:200, 555335), anti-CD14 (BD, control group. 1:200, 563079), anti-CCR7 (BD, 1:200, 557734), anti-CLEC9A (BD, 1:200, 564266) and anti-CD1c (BD, 1:200, 564900) were used to confirm the subgroups of myeloid Trajectory and RNA velocity analysis. To map differentiation in the TME, lineage. Data analysis was performed in FlowJo (V10). pseudotime analysis was performed with Monocle28 to determine the dramatic translational relationships among cell types and clusters. Further detection with the Co-culture colony formation experiments. T24 and EJ bladder urothelial carci- Monocle2 plot_pseudotime_heatmap function revealed the key role of a series of noma cell lines were obtained from American Type Culture Collection (ATCC) genes in the differentiation progress. Signifcantly changed genes were identified by and were cultured according to standard protocols. A co-culture experiment was the differential GeneTest function in Monocle2 with a q-value < 0.01. performed by seeding bladder cancer cells (2 × 103) in the lower chamber and RNA velocity was performed to investigate potential inter-relationship of iCAFs (1 × 105) in the upper chamber of a 6-well transwell apparatus with a 0.4 µm myeloid lineage. BAM file containing all the myeloid cells was used in this pipeline. pore size (Corning Incorporated, NY, USA), and the cells were cultured together All the parameters were set as default. he result was visualized into UMAP plot. for 7 days. Subsequently, colonies were fixed with 4% paraformaldehyde and then were stained with crystal violet. The areas of the colonies were estimated by ImageJ. Simultaneous gene regulatory network analysis. CENIC30 is a new computa- Student’s t tests were used to detect differences between the control and co-culture tional method used in the construction of regulatory networks and in the groups.

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48. Rebouissou, S. et al. EGFR as a potential therapeutic target for a subset of Author contributions muscle-invasive bladder cancers presenting a basal-like phenotype. Sci. Transl. K.C. and Z.C. conceived the idea. L.Z. collected the specimen and prepared single-cell Med. 6, 244ra291 (2014). suspension for sequencing. J.H. finished the bioinformatics analysis. J.H., L.L., Y.H. 49. Biton, A. et al. Independent component analysis uncovers the landscape of the finished the FACS sorting and co-culture assay. M.X., Y.Y. helped with immuno- bladder tumor transcriptome and reveals insights into luminal and basal fluorescence staining, J.H. and K.C. wrote the manuscript. All authors reviewed and subtypes. Cell Rep. 9, 1235–1245 (2014). approved the manuscript. 50. Choi, W. et al. Identification of distinct basal and luminal subtypes of muscle- invasive bladder cancer with different sensitivities to frontline chemotherapy. Competing interests Cancer cell 25, 152–165 (2014). 51. Sjödahl, G. et al. A molecular taxonomy for urothelial carcinoma. Clin. Cancer The authors declare no competing interests. Res. 18, 3377–3386 (2012). 52. Lee, J. S. et al. Expression signature of E2F1 and its associated genes predict Additional information superficial to invasive progression of bladder tumors. J. Clin. Oncol. 28, Supplementary information is available for this paper at https://doi.org/10.1038/s41467- 2660–2667 (2010). 020-18916-5. 53. McConkey, D. J. et al. A Prognostic gene expression signature in the molecular classification of chemotherapy-naïve urothelial cancer is predictive of clinical Correspondence and requests for materials should be addressed to J.H. or K.C. outcomes from neoadjuvant chemotherapy: a phase 2 trial of dose-dense methotrexate, vinblastine, doxorubicin, and cisplatin with bevacizumab in Peer review information Nature Communications thanks Sergei Kusmartsev and the urothelial cancer. Eur. Urol. 69, 855–862 (2016). other, anonymous reviewers for their contribution to the peer review of this work. Peer 54. Song, B. N. et al. Identification of an immunotherapy-responsive molecular review reports are available. subtype of bladder cancer. EBioMedicine 50, 238–245 (2019). 55. Guo, C. C. et al. Assessment of luminal and basal phenotypes in bladder Reprints and permission information is available at http://www.nature.com/reprints cancer. Sci. Rep. 10, 9743 (2020). 56. van der Heijden, A. G. et al. A five-gene expression signature to predict Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in progression in T1G3 bladder cancer. Eur. J. Cancer 64, 127–136 (2016). published maps and institutional affiliations. 57. Lindgren, D. et al. Integrated genomic and gene expression profiling identifies two major genomic circuits in urothelial carcinoma. PloS ONE 7, e38863 (2012). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, Acknowledgements adaptation, distribution and reproduction in any medium or format, as long as you give The authors thank OE Biotech Co., Ltd (Shanghai, China) for providing single-cell appropriate credit to the original author(s) and the source, provide a link to the Creative RNA-seq and Dr. Yongbing Ba and Yao Lu for assistance with bioinformatics analysis. Commons license, and indicate if changes were made. The images or other third party We thank Bo Zhang from NovelBioinformatics Ltd., Co. for the support of bioinfor- material in this article are included in the article’s Creative Commons license, unless matics analysis with their NovelBrain Cloud Analysis Platform (www.novelbrain.com). indicated otherwise in a credit line to the material. If material is not included in the We also thank Dr. Yichen Lei of Ruijin Hospital for help of flow cytometry. We also article’s Creative Commons license and your intended use is not permitted by statutory thank Dr. Jianming Zeng(University of Macau), and all the members of his bioinfor- regulation or exceeds the permitted use, you will need to obtain permission directly from matics team, biotrainee, for generously sharing their experience and codes. This work the copyright holder. To view a copy of this license, visit http://creativecommons.org/ was supported by grant from National Natural Sciences Foundation of China licenses/by/4.0/. (81672524), Hubei Provincial Natural Science Foundation of China (2018CFA038), Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ20180305164838833) and Fundamental Research Funds for the Central University © The Author(s) 2020, corrected publication 2020 (201kfyRCPY0049).

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