Bellapu, A. | bioRxiv Science

bioRxiv2026

Atherosclerosis destabilizes regulatory T cells (Tregs) resulting in multiple families of exTregs

How regulatory T cells (Tregs) lose lineage identity during chronic inflammation remains poorly understood. Here, using inducible Foxp3 lineage tracing together with single-cell transcriptomic, proteomic and T cell receptor (TCR) profiling in atherosclerosis-prone mice, we identify Treg destabilization as a staged and branching differentiation process rather than an abrupt loss of lineage identity. Conventional Tregs (cTregs) first transition through an effector Treg (eTreg) intermediate characterized by attenuation of the CD25-STAT5 axis while retaining core Treg features, before diversifying into eight transcriptionally distinct exTreg states, including Tfh-like, cytotoxic, Th1-like inflammatory, Th1-like cytotoxic and proliferative populations. Trajectory inference, TCR clonotype analysis and experimental Treg-to-exTreg conversion independently converged on this developmental framework, revealing that clonally related exTregs acquire distinct effector programs. Mechanistically, we identify Treg-intrinsic IL-6R signaling as an important driver of this process. IL-6 accelerated exTreg generation in vitro, whereas Treg-specific deletion of Il6ra reduced inflammatory exTreg differentiation and attenuated atherosclerosis in vivo. Together, these findings establish a framework for Treg destabilization during atherosclerosis and provide a conceptual basis for preserving Treg lineage stability in chronic inflammatory disease.

immunology↗

bioRxiv2026

scDIG: An R Shiny Application for Interactive Density-Based Gating of Single-Cell Proteomic and Transcriptomic Data

Delineating biologically meaningful cell populations within single-cell embedding spaces requires methods that balance expert guidance with reproducibility. We present scDIG, a Shiny-based tool that integrates bimodal index-driven feature selection, feature-weighted kernel density estimation, and interactive contour-based gating to define cell populations directly within two-dimensional projections of scRNA-seq and CITE-seq data. We applied scDIG to CITE-seq PBMC data from human subjects in the Cardiovascular Assessment Virginia (CAVA) cohort and show that it resolves transcriptionally distinct CD4+ T cell subpopulations within continuous embeddings that are not readily captured by conventional clustering approaches. These findings demonstrate the utility of scDIG for robust, reproducible classification of single-cell populations and for identifying immunologically relevant effector states. The app is freely available for non-commercial use at https://au-cbgm-shiny.augusta.edu/gating, with source code available at https://gitlab.com/pbombina/scdig.

bioinformatics↗