Fig. 4: Transcriptional signatures and regulatory networks of CD8⁺ CAR T cells at T_exp and T_per.

a Heatmap depicting correlation between pseudo-bulk transcriptome of each sample (patient by timepoint). Samples were ordered along columns and rows by hierarchical clustering. Transcriptomes of day 7 samples from Maus et al. were added for external validation. b Stacked bar graph depicting label transfer of timepoint (T_exp or T_per) from this study’s dataset onto single-cell transcriptomes of samples from Maus et al. c Gene set enrichment analysis comparing CAR T cells between T_exp and T_per. Gene sets were ordered by direction of upregulation and magnitude of enrichment. d Tile map depicting normalized expression of genes (columns) among different cell clusters at T_exp and T_per (rows). Genes were manually grouped into modules according to known functions. e Schematic for regulon construction and T_exp versus T_per classification. After transcriptomes were transformed into regulomes, regulon scores were calculated to train a machine-learning model to classify CAR T cells from T_exp and T_per. Key regulons were identified based on importance for the model’s predictions. Created in BioRender. Hu, Y. (2025) https://BioRender.com/3ltrr3g. f Bar graph depicting the SHapley Additive exPlanation (SHAP) values for the top eight regulons underlying T_exp and T_per predictions. g Tile map depicting normalized signature scores of regulons (columns) among different cell clusters at T_exp and T_per (rows). Regulons were grouped as T_exp-determining (left) and T_per-determining (right). Target networks for the top eight T_exp-determining (h) and T_per-determining regulons (i). In each regulatory network, only the top differentially expressed genes are depicted. Each gene is colored according to log₂ fold-change between expression in T_exp (red) and T_per (blue). Source data are provided as a Source Data file.