Additional file 2 of Single-cell N6-methyladenosine regulator patterns guide intercellular communication of tumor microenvironment that contribute to colorectal cancer progression and immunotherapy
Creators: name: Gao, Yuzhen
name: Wang, Hao
name: Chen, Shipeng
name: An, Rui
name: Chu, Yadong
name: Li, Guoli
name: Wang, Yanzhong
name: Xie, Xinyou
name: Zhang, Jun
Publisher: name: figshare
Year: 2022
Subjects: subject: Biophysics
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Developmental Biology
subject: Cancer
subject: Hematology

description: Additional file 2: Figure S1. The expression of m6A regulators and the association of them with ImmuneScore in CRC (Related to Fig. 1). A The expression of m6A regulators in CRC samples with main TME cell types. B The association of m6A expression with ImmuneScore in different TME cell types in scRNA-seq data. Figure S2. Exploration of cancer-association fibroblast cells in CRC. Related to Fig. 2. A t-SNE plot for stromal cells reveals different distribution of fibroblasts and non-fibroblasts between normal mucosa (2197 cells) and CRC tissue (2736 cells). B Heatmap showing the top four genes in normal mucosa and tumor fibroblasts. C The prognosis of fibroblast cells by xCell in TCGA-COAD and READ. D Trajectory analysis for cancer-association fibroblast cells. E Top genes in four NMF clusters for 3462 fibroblast cells, including m6A-fib-C1 (1939 cells, HNRNPA2B1 dominant), m6A-fib-C2 (245 cells, WTAP dominant), m6A-fib-C3 (1194 cells, HNRNPC dominant), and m6A-fib-C4 (84 cells, no m6A methylation). F Heatmap showing the average expression of cell surface protein genes in four m6A fibroblast cell clusters (Kruskal–Wallis test, p &lt; 0.001). Figure S3. Heatmap of pseudotime Trajectory analysis for TME cell subtypes in TME main cell types of CRC, including macrophage, and B cells, as well as four types of T cells (CD8 + T, CD4 + T, Treg, and NK T cells). Figure S4. Features of m6A-mac clusters in CRC. Related to Fig. 3. A t-SNE plot of 5822 macrophages by their source class in SMC dataset. B Heatmap showing the correlation of four m6A methylation regulator clusters by NMF, named as methy-mac-C1 (n = 1432), methy-mac-C2 (n = 1538), methy-mac-C3 (n = 2169), and methy-mac-C4 (n = 442) for 5822 macrophage cells. C Heatmap showing the different expressions for m6A methylation regulators in macrophages cells. D Bar plot showing the number and percentage of methy-mac clusters, including the cluster without m6A methylation regulator expression between tumor and normal samples. E and F The m6A-mac clusters were related to proinflammatory, proliferating and SPP1 + macrophage cells. G The distribution of checkpoints gene expression among five m6A-mac clusters. H The activity of hallmark pathways among five m6A-mac clusters. Figure S5. The immune features and prognosis of m6A-T cell clusters in CRC. Related to Fig. 4. A The distribution of checkpoints gene expression among m6A-related T sub cluster cells, including CD4+, Cd8 + NK, and Regulatory T cells. B Enrichment cluster analysis for activated signaling ways and functions of m6A-related macrophage types in the Cytoscape by the REAC database. Figure S6. The dynamic effects to the prognosis of m6A-related subtype TME cells in the Pan Cancer patients. Related to Fig. 5. A The significant prognosis of the m6A-related cells, including CAF subtype cells (HNRNPA2B1 + CAF-C1; WTAP + CAF-C2; HNRNPC + CAF-C3; NoneMethy-CAF-C4), macrophage subtype (WTAP + mac-C1;HNRNPA2B1 + mac-C3;YTHDC1 &amp;YTHDF3 + mac_C4), T subtype cells (HNRNPA2B1 + CD8 + T cells-C1;ALKBH5 + CD8 + T cells-C4; HNRNPA2B1 + Reg T cells-C1; YTHDF2 + Reg T cells-C4), and B subtype cells (HNRNPA2B1 + B-C1; WTAP + B-C2; NoneMethy + B-C4). B Tables showed dynamic changes of the number of the prognosis among different subtypes in Pan-Cancer patients.