Deciphering gene regulatory programs underlying functionally divergent naive T cell subsets

Naive CD8+ T cells are a heterogeneous population, with different subsets possessing distinct functions and kinetics upon activation. However, the gene regulatory circuits differentiating these naive subsets are not well studied. In this work, we analyzed a large collection of public and newly generated RNA-seq and ATAC-seq profiles of different subsets of naive CD8+ T cells, revealing significant differences in the gene regulatory landscapes between subsets. We leveraged these data by employing a network inference algorithm, Inferelator, to identify the transcriptional regulatory circuits active in each subset. The predicted transcriptional network of the naive CD8+ T cell pool was validated by multiple orthogonal approaches, including CUT&Tag and Micro-C. Interestingly, our network analysis revealed a novel role for Eomes in promoting effector cell differentiation in specific cell subsets. Moreover, we uncovered multiple novel regulators across a variety of subsets and discovered several modules of genes that were co-regulated by shared sets of transcription factors in distinct subsets. Collectively, our data defines the gene regulatory programs differentiating naive CD8+ T cells and facilitates the identification of novel transcription factors that may alter the propensity of naive CD8+ T cells to become effector or memory cells after infection.