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Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression

Nature Cell Biology volume 23, pages 87–98 (2021)Cite this article

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Abstract

Prostate cancer shows remarkable clinical heterogeneity, which manifests in spatial and clonal genomic diversity. By contrast, the transcriptomic heterogeneity of prostate tumours is poorly understood. Here we have profiled the transcriptomes of 36,424 single cells from 13 prostate tumours and identified the epithelial cells underlying disease aggressiveness. The tumour microenvironment (TME) showed activation of multiple progression-associated transcriptomic programs. Notably, we observed promiscuous KLK3 expression and validated the ability of cancer cells in altering T-cell transcriptomes. Profiling of a primary tumour and two matched lymph nodes provided evidence that KLK3 ectopic expression is associated with micrometastases. Close cell–cell communication exists among cells. We identified an endothelial subset harbouring active communication (activated endothelial cells, aECs) with tumour cells. Together with sequencing of an additional 11 samples, we showed that aECs are enriched in castration-resistant prostate cancer and promote cancer cell invasion. Finally, we created a user-friendly web interface for users to explore the sequenced data.

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Fig. 1: Overview of the single-cell landscape for prostate cancer.
Fig. 2: Purified signature derived from single-cell data and the association with survival.
Fig. 3: scRNA-seq reveals heterogeneity in immune components.
Fig. 4: Tumour-derived EVs convey ectopic KLK3 expression in T cells.
Fig. 5: Identifying an activated EC cell subset in stromal cells.
Fig. 6: aEC cells undergo ECM remodelling and are enriched in CRPC.

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Data availability

Data have been deposited in the Gene Expression Omnibus (GEO) under accession no. GSE141445 and the Genome Sequence Archive for Human (GSA-Human) under accession HRA000312 and can be accessed at www.pradcellatlas.com. For gene expression analysis in T cells, scRNA-seq data from the following NCBI GEO accessions were used: GSE99254 (NSCLC)66, GSE108989 (CRC)67 GSE98638 (HCC)51 and GSE103322 (HNSCC)10. For survival analysis, bulk RNA-seq data from the following studies were used: TCGA (333 samples, http://firebrowse.org/?cohort=PRAD)4, ref. 17 (131 samples, MSKCC, https://doi.org/10.1016/j.ccr.2010.05.026), ref. 68 (294 samples, GSE70770), ref. 69 (79 samples, https://doi.org/10.1172/JCI20032/) and Changhai 2020 (136 samples, www.cpgea.com)70. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

All R packages used are available online, as described in the Methods. Customized code for data analysis and plotting are available on GitHub (https://github.com/chensujun/scRNA).

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Acknowledgements

This work was supported by the National Key R&D Plan of the China Precision Medicine Project (2017YFC0908002 to S.R.), the National Natural Science Foundation of China (81872105 to S.R.), the Princess Margaret Cancer Foundation (886012001223 to H.H.H.), the Canada Foundation for Innovation and Ontario Research Fund (CFI32372 to H.H.H.), a NSERC discovery grant (498706 to H.H.H.), a Canadian Cancer Society Innovation Grant (703800 to H.H.H.), Prostate Cancer Canada (TAG2018-2061, RS2016-1022 and D2016-1115 to H.H.H.), CIHR operating grants (142246, 152863, 152864 and 159567 to H.H.H.) and a Terry Fox New Frontiers Program Project Grant (1090 P3 to H.H.H.). H.H.H. was supported by an OMIR Early Researcher Award and CIHR New Investigator Award. H.H.H. holds the Joey and Toby Tanenbaum Brazilian Ball Chair in Prostate Cancer. J.W. was partially funded by the National Natural Funding of China (81272404, 81772806 and 81972744). G.Z. and S.W. were supported by the Sanming Project of Medicine in Shenzhen (SZSM201601043). Y.L. was funded by the National Natural Science Foundation of China (31801111) and the Dream Mentor–Outstanding Young Talent Program (fkyq1910). P.C.B. was supported by the NIH/NCI under award nos. P30CA016042, 1U01CA214194-01 and 1U24CA248265-01. We thank L.W. Chung from Cedars-Sinai for providing the C4-2B cell line.

Author information

Author notes

  1. These authors contributed equally: Sujun Chen, Guanghui Zhu, Yue Yang, Fubo Wang.

Authors and Affiliations

  1. Department of Urology, Shanghai Changhai Hospital, Shanghai, P. R. China

    Sujun Chen, Guanghui Zhu, Yue Yang, Fubo Wang, Yu-Tian Xiao, Yasheng Zhu, Yongwei Yu, Keqin Dong & Shancheng Ren

  2. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Sujun Chen, Guanghui Zhu, Fraser Soares, Helen Loo Yau, Michael Fraser, Daniel D. De Carvalho, Theodorus van der Kwast, Alejandro Berlin & Housheng Hansen He

  3. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Sujun Chen, Helen Loo Yau, Daniel D. De Carvalho, Paul C. Boutros & Housheng Hansen He

  4. Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    Sujun Chen

  5. Urology Institute of Shenzhen University, The Third Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, P. R. China

    Guanghui Zhu & Song Wu

  6. Department of Urological Surgery, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, Shenzhen, P. R. China

    Guanghui Zhu & Song Wu

  7. East China Normal University, Shanghai, P. R. China

    Na Zhang, Zhengfang Yi & Mingyao Liu

  8. Cancer Institute, Shanghai Urological Cancer Institute, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, P. R. China

    Xiaojie Bian & Jianhua Wang

  9. Department of Urology, Cancer Hospital, Chinese Academy of Medical Sciences, Shanghai, P. R. China

    Fei Liu

  10. Department of Surgery, Division of Cancer Biology and Therapeutics, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Javier Mariscal & Dolores Di Vizio

  11. Department of Laboratory Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China

    Yin Liu

  12. Novel Bioinformatics Co., Ltd, Shanghai, P. R. China

    Bo Zhang, Weidong Chen, Chao Wang, Dai Chen & Qinghua Guo

  13. Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  14. Department of Urology, University of California Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  15. Jonsson Comprehensive Cancer Centre, University of California Los Angeles, Los Angeles, USA

    Paul C. Boutros

  16. Institute for Precision Health, University of California Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  17. Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada

    Paul C. Boutros

  18. Samuel Oschin Comprehensive Cancer Institute, Division of Cancer Biology and Therapeutics, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Dolores Di Vizio

  19. Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Dolores Di Vizio

  20. Department of Biomedical Sciences, Division of Cancer Biology and Therapeutics, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Dolores Di Vizio

  21. Department of Pathology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, P. R. China

    Zhou Jiang

  22. Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada

    Alejandro Berlin

Authors
  1. Sujun Chen
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  2. Guanghui Zhu
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Contributions

The studies were designed by S.C., G.Z., J.W., M.F., D.D.V., H.H.H. and S.R. Experiments were performed by Y.-T.X., N.Z., X.B., Y. Yang, F.W., C.W., Y.Z., Y. Yu, K.D., J.M., Y.L., F.S., H.L.Y., M.L. and W.C. Data analysis was carried out by S.C., G.Z., B.Z., F.L., W.C., D.C., Q.G., Z.Y., S.W., M.F., P.C.B., D.D.D.C., T.v.d.K., Z.J. and A.B. The first draft of the manuscript was written by S.C., G.Z., S.R. and H.H.H. All authors revised and approved the manuscript.

Corresponding authors

Correspondence to Jianhua Wang, Housheng Hansen He or Shancheng Ren.

Ethics declarations

Competing interests

B.Z., W.C., C.W., D.C. and Q.G. are co-founders for Novel Bioinformatics Co., Ltd. All other authors declare no competing interests.

Additional information

Peer review information Nature Cell Biology thanks Mark A. Rubin for his contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Single cell characterization and CNV inference analysis of prostate tumours.

tSNE view of 36,424 single cells, color coded by sample (a), cluster (b) and broad lineage (c). d, Inferred CNA for cells in tumour 156. e, the CNA score and correlation for each cell in the indicated sample. Red, CNA score > 0.04 and CNA correlation >0.4; blue, CNA score <0.04 and CNA correlation <0.4; black, all remaining cells. f, Percentage of genes showing strong CNV (averaged CNA score > 0.1 in putative malignant cells) in our data (PCa, n = 13 tumor samples) and that from Tirosh et al. (Mel, n = 14 tumor samples). P value calculated with two-sided Mann-Whitney U test. g, tSNE view of 36,424 single cells, color coded by inferred cell malignancy identity. For box plots, center line represents the median and box limits represent upper and lower quartiles, and whiskers depicts 1.5× the interquartile range (IQR), extreme values outside of this range is shown as individual points. Statistical data for Extended Data Fig. 1f are provided in the source data.

Source data

Extended Data Fig. 2 Gene expression and signature analysis for different cell types.

a, Heatmap shows the relative abundance of 27 genes in the ‘T cell costimulation’ process in each cell. The top color bar indicates cell types and the left color bar shows the mean UMI of genes. b, tSNE view of 36,424 single cells, color coded by epithelial subtypes. c, Smoothened distribution of PAM50 signature score, cells grouped by annotated cell type. d, Smoothened distribution of high Gleason Score (GS) related signature, cells grouped by annotated cell type. Signature score calculated as the mean of z-score for the 19 high GS related genes identified by Pressinotti et al.20. e, Smoothened distribution of 3 high GS related individual genes; cells grouped by annotated cell type. Y-axis shows normalized UMI (nUMI) in logarithm scale.

Extended Data Fig. 3 Characterizing different epithelial cell derived programs.

a, tSNE view of 23,674 epithelial cells, colour-coded by sample. b, Smoothened distribution for four prostate cancer related signatures in epithelial cell clusters as defined in Fig. 2a. P-values for cluster 10 compared to all other cells for Luminal A, Luminal B are <2.2 × 10−16; CLESs are 0.26 and 0.14, respectively; P-values for cluster 12 compared to all other cells for Luminal A, Luminal B, Hypoxia and PCS1 are <2.2 × 10−16: CLESs are 0.04, 0.79, 0.88 and 0.995, respectively. P values are two-sided and not adjusted for multiple comparisons. c, Smoothened distribution of the indicated signature score, TME and epithelial cells grouped by their assigned cell types and clusters, respectively. P-values (Mann-Whitney U test, two-sided) for cluster 12 CellCycle and cluster 10 basal/intermediate signatures compared to all the rest cells are < 2.2 × 10−16; CLESs are 0.998 and 0.98, respectively. d, GO terms enriched in CellCycle subtype (left) and contingency table showing number of cells in G2/M for CellCycle subtype compared to all the other cells (right), One-sided P value calculated with Fisher’s exact test. OR = 5.7. e, Comparison of BCR-free rate between the high and low groups stratified using CellCycle signature across multiple datasets. P-values are calculated using Cox proportional hazard model (CoxPH) and not adjusted for multiple comparisons. Numbers in brackets show 95% CI for PH.

Extended Data Fig. 4 Characterizing the basal/intermediate signature.

a, Comparison of BCR-free rate between the high and low groups stratified using Basal/Intermediate signature in the indicated datasets. P-values are calculated using Cox proportional hazard model (CoxPH) and not adjusted for multiple comparisons. Numbers in brackets show 95% confidence interval (CI) for hazard ratio (HR). b, Correlation between tumour purity corrected CCL2 expression and basal/intermediate signature in TCGA. c, Correlation between macrophage, T cell and basal/intermediate signature across multiple datasets. Two-sided P values calculated for Spearman’s rank correlation and not adjusted for multiple comparisons.

Extended Data Fig. 5 Single cell transcriptome reveals immune cell heterogeneity.

a, Schematics for all C6 specific incoming signals. b, Percentage of C6 TAM cells in each sample. c, Differentially activated metabolism-related pathways. d, tSNE view of 3,116 T cells, color coded by the average expression of lipid mediator, glycogen metabolism and glycolysis genes. e, Smoothened distribution of AR signature gene abundance, cells grouped by cluster. f, Genes in module 61. Line length and circle size corresponds to expression correlation between KLK3 and the indicated gene. Statistical data relevant to Extended Data Fig. 5b are provided in the source data.

Source data

Extended Data Fig. 6 Analysis of KLK3 expression in T cells.

Flow cytometry sorting strategy (a) and statistics (b) of PSMA+ CD8 T cells. Relative expression of KLK3 in the indicated prostate cancer cell lines c) and EVs (d) derived from them. e, Examination of KLK3 protein (PSA, red) in T cells (CD8+, green) after co-culture with EVs derived from the indicated prostate cancer cell lines by fluorescence microscopy. Scale bars, 10μm. Data show representative results of two repeats. f, Immunoblot for small EV-enriched (CD81) and depleted proteins (GM130) in C4-2B EVs. EVs collected by differential centrifugation followed by density gradient purification. 1µg of protein was loaded for whole cell lysate (WCL). Same volume was loaded for S-EV fractions. g, rtPCR for the indicated probes in C4-2B small EVs collected by differential centrifugation and floated in different density fractions. tSNE view showing cells from the high-risk prostate cancer patient (SC001H), color coded by the tissue source (h) or cell type (i). Distribution of KLK3 expression in different cell types for samples from Batch 1 data (j) (n = 22,667, 7,495 and 629 cells for epi., immune and stroma groups, respectively), tumour tissue (k) (n = 700, 4,038 and 594 cells for epi., immune and stroma groups, respectively) and left LN (l) (n = 153, 1,954 and 2 cells for epi., immune and stroma groups, respectively) from Batch 2. Two-sided P values are calculated using Mann-Whitney U test. Y-axis of j-l represents natural logarithm scale. For box plots, center line represents the median and box limits represent upper and lower quartiles, and whiskers depicts 1.5× the IQR, extreme values outside of this range is shown as individual points. Statistical data relevant to Extended Data Fig. 6c-d, g are provided in the source data. Unprocessed Western Blots relevant to Extended Data Fig. 6f are provided in the source data.

Source data

Extended Data Fig. 7 Single cell characterization of stromal components.

a, Hierarchical clustering using all subtype marker genes, color coded by subtypes. b, Correlation between EMT score in epithelial cells and percentage of ACTA2+ CAF. Two-sided P values calculated for Spearman’s rank correlation and not adjusted for multiple comparisons. c, tSNE view CAF cells, color coded by activation level of the indicated TFs (AUC). d, Overlap of the unique incoming cell communication pairs from epithelial cells to fibroblast, regular EC and aEC. e, Top 5 most enriched pathways for each cluster.

Supplementary information

Reporting Summary

Supplementary Table

Supplementary Table 1. Patient sample clinical information. Supplementary Table 2. GO enrichment for each co-expression module shown in Fig. 3h. Supplementary Table 3. GO enrichment for the top 150 marker genes for each CAF subtype. Related to Extended Data Fig. 7b.

Source data

Source Data Fig. 4

Statistical source data relevant to Fig. 4b–d.

Source Data Fig. 6

Statistical source data relevant to Fig. 6g,h.

Source Data Extended Data Fig. 1

Statistical source data relevant to Extended Data Fig. 1f.

Source Data Extended Data Fig. 5

Statistical source data relevant to Extended Data Fig. 5b.

Source Data Extended Data Fig. 6

Statistical source data relevant to Extended Data Fig. 6c,d and f,g.

Source Data Extended Data Fig. 6

Unprocessed western blots relevant to Extended Data Fig. 6f.

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Chen, S., Zhu, G., Yang, Y. et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat Cell Biol 23, 87–98 (2021). https://doi.org/10.1038/s41556-020-00613-6

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