Fig. 3

Characterization of distinct functions and diversely activated transcription factors within TAM subsets. a Dot plot indicating the expression of T cell exhaustion markers in each cluster of the glioma single cell dataset. b–d Spearman correlation between signature scores of the top five markers in TAM-AIF1 (C1QB, C1QA, HLA-DRB1, AIF1, CD74), TAM-CCL3 (CCL3, CCL4, IL1B, CCL3L1, CCL4L2), TAM-SPP1 (SPP1, FTL, APOC1, S100A11, APOC2), and the signature score of immune checkpoints (CD274, PDCD1LG2, CTLA4, PDCD1, LAG3, TIGIT) in the TCGA glioma dataset. e Analysis of TIDE, T cell dysfunction and T cell exclusion scores calculated by the TIDE algorithm in TCGA glioma patients with high and low marker scores for TAM-SPP1. f, g, h Violin plots showing expression of three TFs (BCL3, NFKB2, and MEF2C), selected as examples of enriched TFs in TAM-CCL3 or TAM-AIF1. i–k, m UMAP plots showing the regulon activity for TFs at the single-cell resolution, with cells having AUC scores higher than the threshold highlighted. l Heatmap showing top regulon activity in each TAM subtype. Statistical significance was determined by two-tailed Spearman correlation between variables for (b–d), and by unpaired two-tailed Student’s t-test for (e). **p < 0.01; ****p < 0.0001