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

Multiomics atlas-assisted discovery of transcription factors enables specific cell state programming

The same types of cells can assume diverse states with varying functionalities. Effective cell therapy can be achieved by specifically driving a desirable cell state, which requires the elucidation of key transcription factors (TFs). Here, we integrated epigenomic and transcriptomic data at the systems level to identify TFs that define different CD8 + T cell states in an unbiased manner. These TF profiles can be used for cell state programming that aims to maximize the therapeutic potential of T cells. For example, T cells can be programmed to avoid a terminal exhaustion state (Tex Term ), a dysfunctional T cell state that is often found in tumors or chronic infections. However, Tex Term exhibits high similarity with the beneficial tissue-resident memory T states (T RM ) in terms of their locations and transcription profiles. Our bioinformatic analysis predicted Zscan20 , a novel TF, to be uniquely active in Tex Term . Consistently, Zscan20 knock-out thwarted the differentiation of Tex Term in vivo , but not that of T RM . Furthermore, perturbation of Zscan20 programs T cells into an effector-like state that confers superior tumor and virus control and synergizes with immune checkpoint therapy. We also identified Jdp2 and Nfil3 as powerful Tex Term drivers. In short, our multiomics-based approach discovered novel TFs that enhance anti-tumor immunity, and enable highly effective cell state programming.

One sentence summary

Multiomics atlas enables the systematic identification of cell-state specifying transcription factors for therapeutic cell state programming.

Computational tools for inferring transcription factor activity

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

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

T cells develop from multipotent progenitors by a gradual process dependent on intrathymic Notch signaling and coupled with extensive proliferation. The stages leading them to T-cell lineage commitment are well characterized by single-cell and bulk RNA analyses of sorted populations and by direct measurements of precursor-product relationships. This process depends not only on Notch signaling but also on multiple transcription factors, some associated with stemness and multipotency, some with alternative lineages, and others associated with T-cell fate. These factors interact in opposing or semi-independent T cell gene regulatory network (GRN) subcircuits that are increasingly well defined. A newly comprehensive picture of this network has emerged. Importantly, because key factors in the GRN can bind to markedly different genomic sites at one stage than they do at other stages, the genes they significantly regulate are also stage-specific. Global transcriptome analyses of perturbations have revealed an underlying modular structure to the T-cell commitment GRN, separating decisions to lose "stem-ness" from decisions to block alternative fates. Finally, the updated network sheds light on the intimate relationship between the T-cell program, which depends on the thymus, and the innate lymphoid cell (ILC) program, which does not.

TET Proteins in the Spotlight: Emerging Concepts of Epigenetic Regulation in T Cell Biology

Ten-eleven translocation (TET) proteins are dioxygenases that oxidize 5-methylcytosine to form 5-hydroxymethylcytosine and downstream oxidized modified cytosines. In the past decade, intensive research established that TET-mediated DNA demethylation is critical for immune cell development and function. In this study, we discuss major advances regarding the role of TET proteins in regulating gene expression in the context of T cell lineage specification, function, and proliferation. Then, we focus on open questions in the field. We discuss recent findings regarding the diverse roles of TET proteins in other systems, and we ask how these findings might relate to T cell biology. Finally, we ask how this tremendous progress on understanding the multifaceted roles of TET proteins in shaping T cell identity and function can be translated to improve outcomes of human disease, such as hematological malignancies and immune response to cancer.