Single-Cell Multi-Omics Reveals T Cell Dysregulation in Sarcoidosis 2310194

Abstract Introduction Sarcoidosis is a systemic inflammatory disease characterized by granulomatous inflammation that affects multiple organs. Prior studies suggest that infection may trigger an antigen-mediated T cell response. However, the underlying immunological mechanisms remain largely unclear. Understanding these mechanisms is needed to identify biomarkers and new treatment targets. Methods We established a systems immunology framework to integrate single-cell RNA sequencing and T cell receptor profiling to unravel the landscape of sarcoidosis. We analyzed immune cell composition, cell—cell communication, and signaling pathways in patients with sarcoidosis and healthy controls. We also used an independent public dataset to test changes in immune cell abundance. In addition, we performed pseudo bulk analysis to study signaling changes in regulatory T cells. Results We identified major immune alterations in sarcoidosis. γδT cells were enriched, while Treg cells were reduced, in patients compared to controls. Cell-cell communication analysis revealed increased signaling pathways through immune checkpoint and innate-like pathways, including HLA-E—CD94/NKG2A and ANXA1-FPR1. In contrast, signaling linked to leukocyte recruitment were reduced. Treg cells exhibited diminished outgoing signals in antigen presentation and immune regulation pathways. In an independent dataset, Treg abundance was significantly reduced in patients. Further pseudobulk analysis also showed a significant downregulation of the KLRB1/CD161 signaling, which strengthens our predicted changes in cell-cell communication. Conclusion This study defines a distinct immune dysregulation pattern in sarcoidosis marked by loss of regulatory T cell function and altered T cell communication. These findings provide new insight into disease mechanisms and point to specific immune pathways that may serve as biomarkers or therapeutic targets. Ongoing analysis of T cell receptor data will help identify disease-related antigens and clonal T cell features. Funding Source The authors declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by startup funds from the Immunology Center of Georgia (IMMCG), Medical College of Georgia, Augusta Universi Topic Categories Computational and Systems Immunology (COMP)