Data from: Allospecific splenic Tr1 cells drive effector T cell exhaustion through upregulated Areg-EGFR signaling to promote transplant tolerance
Data files
Dec 17, 2025 version files 397.33 MB
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README.md
1.96 KB
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scripts_and_data_for_dryad.tar.gz
397.32 MB
Abstract
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 a Nur77-dependent pathway. These findings point to the splenic Areg⁺Tr1 cell-EGFR⁺Teff cell axis as a critical immunoregulatory pathway in peripheral transplant tolerance.
Here we provide the raw and processed CyTOF (mass cytometry by time of flight) data and R analysis scripts used to arrive at these conclusions for each cell type mentioned above. CyTOF data is provided as FCS-format files representing counts for thousands of cells and up to 36 mass-tagged protein markers.
Dataset DOI: 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_cellsMHCI+_CD4_T_cellsMHCI+_PD-1hiMHCI+_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 file (R script)
- reads in raw FCS files, arcsinh transform them, perform centroid correction, and write transformed files.
make_seurat_[cell_type].txt: flat text file (R script)
- reads in transformed FCS files and metadata, creating a combined Seurat objects containing each tissue for each animal
sample_ref_[cell_type].txt - flat text file (TSV) containing sample metadata used in make_seurat_[cell_type].txt script. Columns explained below
- file - name of the .fcs CyTOF file
- tissue - tissue of origin (spleen (spl) or peripheral blood (pbl))
- cohort - experimental group of animal
- animal - individual animal ID
- batch - batch that CyTOF data was collected in
all_data_[cell_type].rds: RData file
- Seurat object containing transformed marker counts for each tissue in each individual
Code/software and versions
R version 4+
Seurat version 4.3+
Necessary packages are shown in the R script files
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.