Systems-level identification of key transcription factors in immune cell specification

Abstract

Transcription factors (TFs) are crucial for regulating cell differentiation during the development of the immune system. However, the key TFs for orchestrating the specification of distinct immune cells are not fully understood. Here, we integrated the transcriptomic and epigenomic measurements in 73 mouse and 61 human primary cell types, respectively, that span the immune cell differentiation pathways. We constructed the cell-type-specific transcriptional regulatory network and assessed the global importance of TFs based on the Taiji framework, which is a method we have previously developed that can infer the global impact of TFs using integrated transcriptomic and epigenetic data. Integrative analysis across cell types revealed putative driver TFs in cell lineage-specific differentiation in both mouse and human systems. We have also identified TF combinations that play important roles in specific developmental stages. Furthermore, we validated the functions of predicted novel TFs in murine CD8+ T cell differentiation and showed the importance of Elf1 and Prdm9 in the effector versus memory T cell fate specification and Kdm2b and Tet3 in promoting differentiation of CD8+ tissue resident memory (Trm) cells, validating the approach. Thus, we have developed a bioinformatic approach that provides a global picture of the regulatory mechanisms that govern cellular differentiation in the immune system and aids the discovery of novel mechanisms in cell fate decisions.

Multi-modular structure of the gene regulatory network for specification and commitment of murine T cells

“…Potential transcription factor inputs to active regulatory elements can also be mapped in the DNA even without direct evidence of transcription factor binding, based on the enrichment of their motifs in accessible chromatin in a given cell type ( 32 – 34 ). Mapping open chromatin by DNase-seq or ATAC-seq and using the cell type-specific enrichment of motifs predicted to be bound by given transcription factors ( 35 – 37 ) in the open chromatin has become a powerful way to predict which transcription factors may be important in that cell type ( 38 – 44 ). Even without any evidence that a particular target gene actually responds to the presence of the transcription factor itself, such predictive analysis can be a valuable step toward perceiving network relationships at a global level ( 39 , 45 ).…”

Multi-Omics Atlas-Assisted Discovery of Transcription Factors for Selective T Cell State Programming

“…While our knowledge of individual T cell states has sharply increased and comparative analysis between exhaustion versus memory has been done ( 20 ), systems-level understanding of T cell state landscape and cell-state specifying TFs network remains minimal. We previously performed a global analysis of TF specification for hard-wired cell types in embryonic development ( 21, 22 ) and hematopoietic lineage ( 23 ). As different cell states share a large number of same cell type-defining TFs ( 24 ), an advanced and precise bioinformatics approach is needed to identify TFs that specify cell states.…”

Computational tools for inferring transcription factor activity

“…Similarly, Liu et al. used TFA analysis to identify TFs that promote the differentiation of T cells into effector and memory cells [85]. A specific application of TFs’ differentiation potential is directed reprogramming.…”