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. 2022 Jul 6;20(1):302.
doi: 10.1186/s12967-022-03510-8.

Spatial transcriptomics atlas reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment components in colorectal cancer

Zhiwei Peng  1 , Manping Ye  2 , Huiming Ding  1 , Zhenyou Feng  1 , Kongwang Hu  3
Affiliations

Spatial transcriptomics atlas reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment components in colorectal cancer

Zhiwei Peng et al. J Transl Med. .

Abstract

Background: The tumor-promoting role of tumor microenvironment (TME) in colorectal cancer has been widely investigated in cancer biology. Cancer-associated fibroblasts (CAFs), as the main stromal component in TME, play an important role in promoting tumor progression and metastasis. Hence, we explored the crosstalk between CAFs and microenvironment in the pathogenesis of colorectal cancer in order to provide basis for precision therapy.

Methods: We integrated spatial transcriptomics (ST) and bulk-RNA sequencing datasets to explore the functions of CAFs in the microenvironment of CRC. In detail, single sample gene set enrichment analysis (ssGSEA), gene set variation analysis (GSVA), pseudotime analysis and cell proportion analysis were utilized to identify the cell types and functions of each cell cluster. Immunofluorescence and immunohistochemistry were applied to confirm the results based on bioinformatics analysis.

Results: We profiled the tumor heterogeneity landscape and identified two distinct types of CAFs, which myo-cancer-associated fibroblasts (mCAFs) is associated with myofibroblast-like cells and inflammatory-cancer-associated fibroblasts (iCAFs) is related to immune inflammation. When we carried out functional analysis of two types of CAFs, we uncovered an extensive crosstalk between iCAFs and stromal components in TME to promote tumor progression and metastasis. Noticeable, some anti-tumor immune cells such as NK cells, monocytes were significantly reduced in iCAFs-enriched cluster. Then, ssGSEA analysis results showed that iCAFs were related to EMT, lipid metabolism and bile acid metabolism etc. Besides, when we explored the relationship of chemotherapy and microenvironment, we detected that iCAFs influenced immunosuppressive cells and lipid metabolism reprogramming in patient who underwent chemotherapy. Additionally, we identified the clinical role of iCAFs through a public database and confirmed it were related to poor prognosis.

Conclusions: In summary, we identified two types of CAFs using integrated data and explored their functional significance in TME. This in-depth understanding of CAFs in microenvironment may help us to elucidate its cancer-promoting functions and offer hints for therapeutic studies.

Keywords: Cancer-associated fibroblasts; Colorectal cancer; Spatial transcriptomics; Tumor microenvironment.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Spatial transcriptomics (ST) to identify intra-tumor heterogeneity in colorectal cancer. A Spatial images of unsupervised clustering results (left) and hematoxylin–eosin staining (H&E) sections annotations (right). B, C UMAP plot of cell types and PCA clustering result profiled in the presenting work. D The expression level of cell markers across different morphological regions through the Dot plot. E Spatial plots show the spatial expression pattern of fibroblast and tumor markers (COL1A1, EPCAM) in this study. F Heatmap of cell-type scores estimated by ssGSEA
Fig. 2
Fig. 2
CAFs-enriched subgroups are identified in CRC. A UMAP plot of unsupervised clustering result of tumor subclusters. B Heatmap of differentially expressed genes (DEGs) of different Tumor-subclusters. C Immunofluorescence analysis of specific proteins expression of iCAFs and mCAFs. D Heatmap of tumor microenvironment (TME) cell-type scores calculated by ssGSEA in tumor region. E KEGG enrichment functions of up-regulated genes in Tumor-subcluster 0. F Violin plots show the expression of tumor, inflammatory-cancer-associated fibroblasts (iCAFs) and myo-cancer-associated fibroblasts (mCAFs) markers in tumor region. G Heatmap of tumor microenvironment (TME) cell-type scores calculated by ssGSEA in fibroblast region
Fig. 3
Fig. 3
iCAFs promote oncogenesis by altering tumor microenvironment. A Heatmap shows ssGSEA result to explore the functional roles of different fibro-subclusters. B, C Trajectory of differentiation of different Fibro-subclusters predicted by monocle 2. D, E The differential expression of EPCAM in distinct Fibro-subclusters. F Gene expression level in single spot ordered along the pseudotime for EMT markers
Fig. 4
Fig. 4
Chemotherapy alters the tumor microenvironment. A, B UMAP plots show patient-specific spatial transcriptomics (ST) expression patterns between colon1 and colon2, A UMAP plot of ST data after CCA, B UMAP plot of ST data without CCA. C Cell compositions predicted in different patients by ssGSEA, predicted cell compositions in all clusters (left), predicted cell composition in fibroblast cluster (middle), predicted cell composition in tumor cluster (right). D Boxplots show the cell compositions of iCAFs, NK cells and monocytes in all clusters. E, F Box plots show the patient-specific metabolism patterns scored by ssGSEA. p value < 0.05 was considered as cut-off criteria
Fig. 5
Fig. 5
iCAFs is associated with clinical prognosis and immune infiltration. A–D Box plots show the correlations between iCAFs and clinicopathologic features. E, F K-M survival plots show that high iCAFs predicted poor prognosis in TCGA COADREAD cohort, OS: overall survival, DSS: disease specific survival. G Forest map shows that iCAFs is an independent prognostic factor for OS. H Immunohistochemical analysis of PDGFRA expression in colorectal cancer or para-carcinoma tissues. I, J The diagrams show the relationships between iCAFs and immune score as well as stromal score. p value < 0.05 was considered as cut-off criteria
Fig. 6
Fig. 6
Workflow of bioinformatic analysis. Two types of datasets were utilized in this project. ST data was used to explore the role of CAFs in TME within patients with colorectal cancer, while bulk RNA-seq dataset was applied to validate the clinical translational value of CAFs identified in ST data. Then immunofluorescence staining and immunohistochemical was applied to validate the results based on bioinformatics analysis and we confirmed the functions of two CAFs types in CRC
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