Fig. 1: Transcriptional and epigenomic atlas of CD8+ T cell differentiation states and TF identification pipeline.
From: Atlas-guided discovery of transcription factors for T cell programming
a, Diagram summarizing CD8+ T cell trajectories during acute and chronic infection or tumour, highlighting differentiation into various effector, memory and exhaustion states, including parallel TRM and TEXterm lineages with overlapping tissue localization. b, Pearson correlation matrix of batch-effect-corrected RNA-seq datasets. Both colour intensity and circle size indicate correlation strength, with red denoting the highest correlation. c, Workflow of the integrative Taiji analysis. Matched RNA-seq and ATAC-seq datasets3,9,17,22,31,32,33,34,35 were used to construct a regulatory network and calculate TF activity scores using PageRank. Downstream analysis included identification of single- and multi-state TFs, TF ‘waves’ and network communities. d–h, TFs (rows) and samples (columns) are displayed as z-normalized PageRank heatmaps. Each column corresponds to a dataset. d,e, PageRank scores of genes encoding 136 single-state TFs (d) and 173 multi-state TFs (e). f–h, Bubble plots show normalized TF PageRank scores and expression for genes encoding TEXterm-selective (f), TRM -selective (g) and multi-state (h) TFs that are active in both cell states. Circle colour represents the normalized PageRank score (red, high) and circle size indicates log mRNA expression across five datasets. TFs are ordered by P value; validated TF genes are highlighted in grey. i,j, TF ‘waves’ associated with exhaustion (i) or TRM cell differentiation (j), indicating coordinated activity of TF groups during cell state transitions. Sample sizes and statistical details for cell state definitions and TF selection criteria are provided in Extended Data Figs. 1e and 2a, respectively.
