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Spatial single-cell protein landscape reveals vimentinhigh macrophages as immune-suppressive in the microenvironment of hepatocellular carcinoma

Nature Cancer (2024)Cite this article

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Abstract

Tumor microenvironment heterogeneity in hepatocellular carcinoma (HCC) on a spatial single-cell resolution is unclear. Here, we conducted co-detection by indexing to profile the spatial heterogeneity of 401 HCC samples with 36 biomarkers. By parsing the spatial tumor ecosystem of liver cancer, we identified spatial patterns with distinct prognosis and genomic and molecular features, and unveiled the progressive role of vimentin (VIM)high macrophages. Integration analysis with eight independent cohorts demonstrated that the spatial co-occurrence of VIMhigh macrophages and regulatory T cells promotes tumor progression and favors immunotherapy. Functional studies further demonstrated that VIMhigh macrophages enhance the immune-suppressive activity of regulatory T cells by mechanistically increasing the secretion of interleukin-1β. Our data provide deep insights into the heterogeneity of tumor microenvironment architecture and unveil the critical role of VIMhigh macrophages during HCC progression, which holds potential for personalized cancer prevention and drug discovery and reinforces the need to resolve spatial-informed features for cancer treatment.

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Fig. 1: Study design and cell type and phenotype definition.
Fig. 2: A spatial single-cell phenotypic atlas of HCC.
Fig. 3: Definition of tumor patterns in the HCC samples.
Fig. 4: SPs with distinct prognosis in HCC.
Fig. 5: The discriminate features of SPs.
Fig. 6: Co-occurrence of macrophage_VIM+ and Treg cells associated with tumor malignancy behaviors.
Fig. 7: Co-occurrence of macrophage_VIM+ and Treg cells favors immunotherapy in HCC.
Fig. 8: VIMhigh macrophages promote the immune-suppressive ability of Treg cells via IL-1β.

Data availability

The WES data that support the findings of this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession no. PRJNA1056508. The RNA-seq data that support the findings of this study have been deposited in the SRA under accession no. PRJCA022445 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA006377). The ST data generated in this study are publicly available at Mendeley Data (https://data.mendeley.com/datasets/cvnx8jd6jw/1). All raw output files resulting from cell segmentation on the CODEX raw images are publicly available at Mendeley Data (https://data.mendeley.com/datasets/6w2k42kytt/1). The dataset derived from this resource, which supports the findings of this study, is publicly available at Mendeley Data (https://data.mendeley.com/datasets/km2df7y256/2). The published scRNA-seq dataset (cohort 3) has been deposited in the China National GeneBank DataBase under accession no. CNP0000650 (ref. 31). The published ST datasets HCC-R1 and HCC-R2 are available from the corresponding author of the original study upon request34. The published ST datasets HCC-1-N, HCC-5-A and HC-4-L have been deposited in the Genome Sequence Archive under accession no. HRA000437 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA000437) (ref. 32). The published RNA-seq datasets HCCDB6 and HCCDB15 can be downloaded from http://lifeome.net/database/hccdb/home.html (ref. 3). The published RNA-seq datasets for the EHBH cohort are available from the corresponding author of the original study upon reasonable request32. The Molecular Signatures Database can be accessed at https://www.gsea-msigdb.org/gsea/msigdb. The source data for Figs. 1–8 and Extended Data Figs. 1–10 have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The code generated in this study is publicly available at Github (https://github.com/sanrishiguang/ChenleiLab_CODEX_HCC).

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Acknowledgements

This work was supported by the National Key R&D Program of China (no. 2023YFC2507500 to L.C.), the National Natural Science Foundation of China (no. U21A20376 to L.C., no. 82273277 to X.Q., no. 81988101 to H.W. and no. 82372872 to S.Y.), the National Science Foundation of Shanghai (no. 21XD1404600 to L.C., no. 21JC1406600 to H.W., no. 22140901000 to L.C. and no. 21DZ2291900 to H.W.) and the Shanghai Institute of Chinese Engineering Development Strategies (no. 23692123600 to H.W.). We thank the Shanghai Municipal Science and Technology Major Project for their support.

Author information

Author notes

  1. These authors contributed equally: Xinyao Qiu, Tao Zhou, Shuai Li, Jianmin Wu, Jing Tang.

Authors and Affiliations

  1. Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

    Xinyao Qiu, Shuai Yang, Hongyang Wang & Lei Chen

  2. National Center for Liver Cancer, Shanghai, China

    Xinyao Qiu, Tao Zhou, Ji Hu, Siyun Shen & Lei Chen

  3. The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China

    Tao Zhou, Ji Hu, Siyun Shen, Hongyang Wang & Lei Chen

  4. Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China

    Shuai Li, Jianmin Wu, Guosheng Ma, Kaiting Wang & Hongyang Wang

  5. Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Jing Tang

  6. Key Laboratory of Signaling Regulation and Targeting Therapy of Liver Cancer, Ministry of Education, Shanghai, China

    Hongyang Wang & Lei Chen

  7. Shanghai Key Laboratory of Hepatobiliary Tumor Biology (EHBH), Shanghai, China

    Hongyang Wang & Lei Chen

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  1. Xinyao Qiu
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Contributions

X.Q. and J.W. constructed the HCC tissue microarray, conducted the CODEX-related experiments and segmented the images. X.Q., T.Z. and S.L. analyzed the data. X.Q. and T.Z. organized the figures. X.Q. wrote the manuscript. X.Q., S.L. and J.W. conducted the in vitro experiments. G.M. conducted the WES and RNA-seq analyses of the FFPE tissues. J.W. and S.L. collected the clinical information. K.W., S.S., S.Y. and J.H. collected the clinical information. J.T. collected the samples with immunotherapies, analyzed the data produced during the revision process and helped revise the manuscript. H.W. and L.C. designed the research, supervised the study and revised the manuscript.

Corresponding authors

Correspondence to Hongyang Wang or Lei Chen.

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The authors declare no competing interests.

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Nature Cancer thanks Bertram Bengsch, Garry Nolan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Single-cell phenotyping in HCC samples.

a, Other markers in the CODEX panel besides cell type recognition markers were listed. b, UMAP plot of the 13 markers used for cell type definition. For each UMAP plot, 16215 cells were plotted. c, The UMAP of each TMA before batch effect correction. A total of 1048575 cells was plotted. d, The UMAP of each TMA after batch effect correction. A total of 1048575 cells was plotted. e, Clustering by Rphenograph with 36 markers. Heatmap showing the z-scored mean marker expression for each cluster. f, Clustering by Rphenograph with 13 cell type recognition markers. Heatmap showing the z-scored mean marker expression for each cluster. The identified cell type was annotated below. g-j, The recognition of cell phenotypes in macrophages (g), tumor cells (h), CD4 + T cells (i), and CD8 + T cells (j) respectively. Indicated functional markers were used to cluster cells. Heatmap showing the z-scored mean marker expression level for each cell phenotype in indicated cell type. The percentage of each cell phenotype in indicated cell type was presented as a bar plot.

Source data

Extended Data Fig. 2 Stratifying the HCC samples based on cell type composition.

a, Hierarchical clustering on cell type percentage defined 4 groups annotated by different colors. The top panel showed the composition of cell types in each sample, followed by detailed annotations of clinical variables. Two-sided Chi-squared Test. b, The Kaplan–Meier survival curves of the groups were drawn and log-rank tests were done to investigate the overall significance. c, The Kaplan–Meier survival curves of each group vs all other samples. Log-rank tests were done to investigate the significance. OS, overall survival. RFS, relapse-free survival. For all panels, n = 288 biological independent samples; group 1, n = 29; group 2, n = 88; group 3, n = 76; group 4, n = 95.

Source data

Extended Data Fig. 3 Cellular neighborhood (CN) definition in the discovery cohort.

a, Ten CNs were defined based on 11 cell types (CT-based-CNs). The annotation of the CNs was listed. The bar plot represented the composition of cell types in each CN and cell types were annotated by different colors. b, Boxplots showing the overall percentage of each CT-based-CN in the discovery cohort. n = 288. c, Forty CNs were defined based on 72 cell types/phenotypes (CP-based-CNs). The annotation of the CNs was listed. The bar plot represented the composition of cell types in each CN and cell types were annotated by colors in Extended Data Fig. 3a. d, Boxplots showing the overall percentage of each CP-based-CN in discovery cohort. n = 288. e-f, The survival curves of the overall CD4 + T cell percentage (e, n = 288) and the specific CD4 + T cell percentage in CN2_ImmMix_CD8T (f, n = 258) in discovery cohort. The Kaplan–Meier survival curves displayed were computed using the optimal split and analyzed by log-rank tests. e, left panel: n = 43 in high group, n = 245 in low group; right panel: n = 215 in high group, n = 73 in low group. f, left panel: n = 43 in high group, n = 215 in low group; right panel: n = 49 in high group, n = 209 in low group. RFS, relapse-free survival; OS, overall survival. For all panels, n denotes biologically independent samples. For boxplots in b and d, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually.

Source data

Extended Data Fig. 4 Definition of tumor purity level and characteristics of each SP in the discovery cohort.

a, Heatmap showing the hierarchical clustering of CT-based-CNs and the percentage of CT-based-CNs. The right panel showed annotations of defined Tumor Purity Level (TPL). The Kaplan–Meier survival curves of TPL were drawn and log-rank tests were done to investigate the significance. L, n = 95; M, n = 105; H, n = 88. b, Representative images of p53 expression in tumor cells. c, Dot plot showing the percentage of each CP-based-CN in distinct SPs. The value and size of the dot represented the percentage. *p represented it was significantly higher or lower than each other SP. Two-sided Wilcox tests. *p < 0.05. d, Dot plot showing the z-scored percentage of each cell type in distinct SPs. The value and size of the dot represented the z-scored percentage. *p represented it was significantly higher or lower than each other SP. Two-sided Wilcox tests. *p < 0.05. e, The Kaplan–Meier survival curves of SPs in samples of TNM stage I&II and III&IV respectively. Log-rank tests. For analysis of TNM stage I & II: SP-HI, n = 48; SP-LI, n = 64; SP-Endo, n = 26; SP-TIM, n = 44; SP-PF, n = 33. For analysis of TNM stage III & IV: SP-HI, n = 18; SP-LI, n = 23; SP-Endo, n = 9; SP-TIM, n = 13; SP-PF, n = 10. f, The Kaplan–Meier survival curves of each SP vs all other samples. Log-rank tests. n = 288. SP-HI, n = 66; SP-LI, n = 87; SP-Endo, n = 35; SP-TIM, n = 57; SP-PF, n = 43. The exact p value in c, d can be found in Supplementary Table 3. For all panels, n denotes biological independent samples.

Source data

Extended Data Fig. 5 Cell type and cell phenotype definition in the validation cohort.

The cell type definition (a) and cell phenotype definition of CD4 + T cells (b), CD8 + T cells (c), Tumor cells (d), and macrophages (e) in the validation cohort. Related to. Heatmap showing the z-scored mean marker expression level for each cell type/phenotype. The overall percentage of each cell type and cell phenotype in the specific cell type in the validation cohort was presented as a bar plot.

Source data

Extended Data Fig. 6 The verification of cellular neighborhoods (CNs) and Tumor Patterns (TPs) in validation cohort.

a, The definition of the cellular neighborhood (CN) in validation cohort. Forty CNs were defined based on all cell types/phenotypes (CP-based-CNs) in the validation cohort. The annotation of the CNs was listed. The bar plot represented the composition of cell types in each CN. b, The comparison of CP-based-CNs between the validation and discovery cohorts. Heatmap showing the z-scored average cell type/phenotype percentage in each CN. The CNs in discovery cohort were annotated in orange whereas that in validation cohort were in green. c, Heatmap showing the hierarchical clustering of selected tumor phenotype-enriched CP-based-CNs and the z-scored percentage of CP-based-CNs in validation cohort. The left panel showed detailed annotations of defined TPs. n = 98. d, The Kaplan–Meier survival curves of TP_p53 vs other TPs in validation cohort. Log-rank tests were done to investigate the significance. TP-p53, n = 4; other TPs, n = 94. e, The schematic of the analysis pipeline of Fig. 5. For all panels, n denotes biologically independent samples.

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Extended Data Fig. 7 The genomic landscape and molecular characteristics of SP-HI, SP-LI, and SP-PF.

a, Genetic profile and associated clinicopathologic features of 97 HCC patients from SP-HI (n = 36), SP-LI (n = 32) and SP-PF (n = 29) groups in the discovery cohort. b-g, Analysis of bulk RNA-seq of 77 HCC samples from SP-HI (n = 28), SP-LI (n = 28) and SP-PF (n = 21) groups in the discovery cohort. b, Heatmap showed the z-scored estimate scores of stromal cell, immune cell and tumor purity using ESTIMATE deconvolution method. P value was calculated by two-sided Kruskal-Wallis test. c, Heatmap showed the z-scored abundance of different immune and stromal cell populations in SP-HI, SP-LI and SP-PF. MCP-counter deconvolution method. P value was calculated by two-sided Kruskal-Wallis test. d, Bar plot showed the constitution of immune cell populations in SP-HI, SP-LI and SP-PF. CIBERSORT deconvolution method. e, Hallmark pathway enrichment of significantly up-regulated DEGs in SP-HI vs others (left), SP-LI vs others (middle), and SP-PF vs others (right). Over-representation analysis (ORA) with p value adjusted by Benjamini & Hochberg method. f, Volcano plots showed the representative up-regulated DEGs in SP-HI vs others (left), SP-LI vs others (middle), and SP-PF vs others (right). DEGs were defined by two-sided Wald test. P value was adjusted by Benjamini-Hochberg method. g, The predicted molecular subclass of each sample. Two-sided Chi-squared test. For all panels, n denotes biologically independent samples.

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Extended Data Fig. 8 Re-analyzing the published scRNA-seq dataset of HCC.

a, Heatmap of indicated markers in myeloid subtypes. The genes were from Fig. 2c in Sun et al., Cell, 2021. b, UMAP plot of the myeloid populations after reclustering as well as the indicated gene expression levels. Each dot represents a single cell. DC: dendritic cells. For each UMAP plot, 3293 cells were plotted. c, UMAP plot of the CD4 + T cells after reclustering, colored by cluster. TC1-Treg was identified. 7294 cells were plotted. d, UMAP plot of macrophage populations after reclustering, colored by cluster. 1635 cells were plotted. e, UMAP plot macrophage populations colored by VIMhigh and VIMlow Macrophages. 1635 cells were plotted. f, Bar plot showing the constitution of VIMhigh and VIMlow Macrophages in each cluster of macrophage population. g, The signature scores of indicated macrophage functional items from MSigDB in VIMhigh and VIMlow macrophages. h, The survival curves of the signature score of VIMhigh macrophages in independent bulk-seq EHBH (n = 80) and HCCDB15 (n = 351) cohorts. The Kaplan–Meier survival curves displayed were computed using the optimal split and analyzed by log-rank tests. For EHBH cohort, n = 9 in low group, n = 71 in high group. For HCCDB15 cohort, n = 301 in low group, n = 50 in high group. i, Pearson correlations (two-sided) between the signature scores of VIMhigh macrophages and Tregs in two independent bulk-seq cohorts. 95% confidence intervals were shown. EHBH cohort, n = 237. HCCDB15, n = 351. j, The basic feature of the 227312-T ST dataset. For all panels, n denotes biologically independent samples.

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Extended Data Fig. 9 Analysis of independent ST datasets.

a-h, HCC-5-A (Wu et al.). i-n, HCC-R1 (Wang et al.). o-t, HCC-4-L (Wu et al.). a,i and o, Spatial feature plots of the signature score of VIMhigh macrophages (left panel) and Tregs (right panel). b,j and p, Unbiased clustering of ST spots and violin plots showing the signature scores of VIMhigh macrophages (upper panel) and Tregs (lower panel) in clusters. The red rectangle highlighted the cluster with high signature scores of both VIMhigh macrophages and Tregs. c-e, k-l and q-r, The Spearman correlation (two-sided) between the signature scores of VIMhigh macrophages and Tregs in highlighted clusters in Extended Data Fig. 8b,j,p respectively. f-h, m-n and s-t, Gene set enrichment analysis (GSEA) on the signature genes of the highlighted clusters in Extended Data Fig. 8b,j,p respectively. The top 7 pathways were displayed. Two-sided Wilcox tests.

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Extended Data Fig. 10 VIMhigh macrophages promote the immune suppressive ability of Treg via IL-1β.

a, Violin plots showing the expression levels of indicated genes in VIMhigh and VIMlow macrophages. Two-sided Wilcox tests. The p.adj was calculated by Bonferroni correction. b, Spearman correlations (two-sided) between IL1B and the signature score of VIMhigh macrophages in three independent bulk-seq cohorts. 95% confidence intervals were shown. EHBH cohort, n = 237. HCCDB6, n = 445. HCCDB15, n = 400. c, Spearman correlations (two-sided) between IL1B and the signature score of VIMhigh macrophages in the indicated clusters in ST datasets. 95% confidence intervals were shown. d, Representative gating results showing the expression of IL-1β and Vimentin in indicated cells. e-g, Indicated mRNA level of genes related to macrophage differentiation in THP1 (e, n = 4), U937 (f, n = 4), and hMDM (g, n = 3). Two-sided unpaired t-test. h, The successful overexpression of VIM was checked by Western Blot and qRT-PCR in THP1 and U937 cells. Two-sided unpaired t-test. n = 3. i, The secretion level of IL-1β detected in indicated status of U937 Ctrl and VIMOE cells. Two-sided unpaired t-test. n = 3. j, The secretion level of Vimentin detected in indicated status of U937 Ctrl and VIMOE cells. No detected secretion can be observed. n = 3. k, THP1-derived M0 macrophages were treated with conditioned medium from THP1/U937-Ctrl/ VIMOE resting cells. The secretion level of IL-1β was detected. Two-sided unpaired t-test. n = 4. l, Experiments with Tregs directly isolated from PMBCs. Tregs were treated with conditioned medium from THP1-derived M0/M1 macrophages with anti-IL-1β neutralizing antibody/control IgG treatment. The mRNA level (left panel) and the secretion level (right panel) of IL-10 in Tregs were detected upon indicated stimulation. Two-sided unpaired t-test. n = 4 for the left panel and n = 6 for the right panel. m. Representative histogram exhibiting the CFSE signals, related to Fig. 8p. For all panels, n denotes biological replicates for each group. In e-l, data are presented as mean ± SD.

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Qiu, X., Zhou, T., Li, S. et al. Spatial single-cell protein landscape reveals vimentinhigh macrophages as immune-suppressive in the microenvironment of hepatocellular carcinoma. Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00824-y

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