Allospecific splenic Tr1 cells drive effector T cell exhaustion through upregulated Areg-EGFR signaling to promote transplant tolerance

Inducing stable tolerance to transplants remains a challenge in immunology. Previously, we induced tolerance to allogeneic islets in nonhuman primates by preemptive alloantigen delivery to antigen-presenting cells in situ. Here, mass cytometry phenotyping with incorporated donor-derived MHC-I peptide-loaded MHC-II tetramers revealed accumulation of allospecific CD4+ T cell clusters in the spleen of tolerant recipients. Areg+Tr1 regulatory and terminally exhausted EGFRhi T (Tex) cells represented the predominant allospecific subsets. Trajectory analysis showed that antigen-experienced effector memory T cells differentiated into suppressive Areg+Tr1 and EGFR+TOX+Nur77+TCF-1- Tex subsets. Cell-cell communication mapping showed that exhausted and effector memory T cells engaged with allospecific Tr1 cells via the Areg-EGFR axis. Gene silencing studies confirmed that Tr1 cells utilize Areg-EGFR signaling to drive the metabolic suppression and epigenetic reprogramming of CD4⁺ T cells through ..., Raw FCS files were transformed and converted to Seurat objects with custom R scripts (process_raw_X.txt and make_seurat_X.txt files, respectively) using information contained in sample key files (sample_ref_X.txt files). These data are separated into folders comprising the data files and analysis files for each respective cell type group.
, # Data from: Allospecific splenic Tr1 cells drive effector T cell exhaustion through upregulated Areg-EGFR signaling to promote transplant tolerance

Dataset DOI: [10.5061/dryad.wh70rxx2g](https://doi.org/10.5061/dryad.wh70rxx2g)

## Description of the data and file structure

There are four separate directories representing four main cell groupings presented in the paper. Each directory comprises raw and transformed FCS files for the CyTOF data, a sample metadata file, R scripts for the transformation of fcs files and conversion to Seurat R objects, and the R object itself.

### File: scripts_and_data_for_dryad.tar.gz

* **Directories**
* `Total_cells`
* `MHCI+_CD4_T_cells`
* `MHCI+_PD-1hi`
* `MHCI+_Tr1`

## **Contents of each [cell_type] directory:** 

**`raw`:** directory

* untransformed FCS files for each tissue in each individual

**`transformed`**: directory

* arcsinh transformed FCS files for each tissue in each individual

**`process_raw_[cell_type].txt`**: flat text f...,