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Epigenetic landscape, key transcriptional regulators, and in vivo identification of human Tr1 cells [scMultiome]

GSE261033 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 28 samples Submitted 2026/07/10 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 28 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
28 samples: 9 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Epigenetic landscape, key transcriptional regulators, and in vivo identification of human Tr1 cellsScience advances · 2026
Summary (written by the submitters)
Type 1 regulatory T (Tr1) cells are CD4+ T cells with immunoregulatory function, which are induced from mature peripheral CD4+ T cells in response to persistent extra-thymic antigens. The role of human Tr1 cells in disease is not completely understood, because transcription factors that regulate their antigen-driven differentiation are not known, and there is lack of consensus on how to best identify them in the tissue, where antigen interactions occur.
Read the full summary ↓
Here, we leveraged comprehensive bulk and single-cell (sc) transcriptomic, epigenomic, and TCR repertoire profiling of human antigen-induced Tr1 cells, complemented with CRISPR-based functional genomics, to uncover the role of transcription factors IRF4, BATF and MAF in human Tr1 cell differentiation and function. In addition, we identify a Tr1 cell transcriptional signature that can reveal Tr1-like cells in sc-RNA-seq datasets of peripheral blood CD4+ T cells of patients treated with Tr1 therapy, and within tumor-resident CD4+ T cells in patients with clear-cell renal cell carcinoma, triple-negative breast cancer, and colorectal cancer. These data enable further development of Tr1 therapies and Tr1-targeting treatments, and provide new insights into human Tr1 cell biology.
Published in
Epigenetic landscape, key transcriptional regulators, and in vivo identification of human Tr1 cells
Cepika AM, Amaya L, Waichler C et al. · Science advances 2026 · PMID 42430483 · doi:10.1126/sciadv.aec6358
This dataset
Open GSE261033 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE261033_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1084855 and SRA study SRP493718. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42430483) ↗
Study design
9 conditions, mostly in duplicate
GEX, healthy donor BC ×2 ATAC_Day0_d ×2 ATAC_Day3_d ×2 ATAC_Day6_d ×2 ATAC_Day9_d ×2 GE_Day0_d ×2 GE_Day3_d ×2 GE_Day6_d ×2 +11 more

Supports a case/control comparison: 2 samples read as cases, 4 as controls.

How we read this: 9 replicated groups read from 28 sample titles; they account for 18 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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