ImmgenT Regulatory T Cell Atlas 2252825

Abstract

Abstract Introduction CD4+Foxp3+ regulatory T cells (Tregs) are essential mediators of immune tolerance, memory formation, tissue repair, and responses to harmless antigens. Their broad functional repertoire is reflected in extensive phenotypic and spatial diversity. To systematically characterize this complexity, the ImmGenT consortium established a comprehensive single-cell atlas of mouse Tregs across diverse tissues and immune perturbations. Methods We analyzed 658 samples spanning lymphoid and non-lymphoid tissues under steady-state and immune perturbations. Through multi-omic integration, we defined a consistent and reproducible Treg framework across tissues and conditions. In parallel, we developed a 7-marker flow cytometry panel resolving major clusters, enabling broad applications. Results This ontology reframes the resting—effector paradigm as a continuum. Cl.1/rTregs are not restricted to lymphoid organs but extend into non-lymphoid tissues (NLTs), and both compartments are represented by a mixture of the same populations. Classic cl.2/KLRG1+ and cl.8/RORγt+ populations are enriched at barrier sites and within tumors, where they constitute up to ∼30% of TILs, yet remain detectable in secondary lymphoid organs (SLOs). We further identify a previously unappreciated CD49b+CD29+ cluster, rare in barrier tissues but enriched in liver, uterus, and placenta. Under perturbation, the distribution of Tregs entirely reshuffles rather than generating new subsets; for instance, cl.2/KLRG1+ and cl.8/RORγt+ Tregs dominate infected lungs where they are scarce at steady state. Finally, we established a Reference-Based Integration algorithm that enables accurate mapping of Tregs from external datasets, demonstrating broad applicability across studies. Conclusion This atlas provides a unified framework and practical tools to dissect Treg diversity, adaptation, and function across tissues and immune perturbations, supporting community-wide integration and exploration. Funding Source 5R24AI072073-14, Cancer Research Institute. Topic Categories Computational and Systems Immunology (COMP)