GEO series

Sustained A2AR expression and loss paradoxically promote CD8+ T cell exhaustion

GSE310452 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2026/07/15 Platform GPL24247

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 12 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: Mus musculus (mouse)
Samples: 12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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: Sustained A2AR expression and loss paradoxically promote CD8(+) T cell exhaustionProceedings of the National Academy of Sciences of the United States of America · 2026

Summary (written by the submitters)

Although A2AR is a key immunoregulatory receptor that suppresses CD8⁺ T-cell activation in response to elevated extracellular adenosine in inflamed or hypoxic microenvironments, its role in CD8⁺ T cell differentiation and cell-fate decisions during chronic viral infection and cancer remains poorly understood. Using A2AR-eGFP reporter mice, we show that A2AR expression is rapidly induced by TCR stimulation and persists under chronic antigen exposure and hypoxia, with sustained expression strongly associated with terminal exhaustion via the canonical Gαs–cAMP–PKA pathway.

Read the full summary ↓

Paradoxically, A2AR loss does not alleviate exhaustion but instead accelerates differentiation toward the terminally exhausted state. Single-cell multiomics profiling revealed that A2AR deficiency activates CD122 (IL-2Rβ)–dependent signaling, driving T-cell exhaustion. Genetic deletion of CD122 in A2AR-deficient CD8⁺ T cells reduced terminal exhaustion, identifying CD122 signaling as a key mediator of A2AR loss–driven exhaustion. Intriguingly, both sustained A2AR expression and A2AR loss converge to promote T cell exhaustion differentiation through distinct mechanisms. These findings uncover a paradoxical role of A2AR in shaping CD8⁺ T cell fate choices during chronic infection and cancer.

Published in

Sustained A2AR expression and loss paradoxically promote CD8(+) T cell exhaustion

Song L, Kharel A, Xie P et al. · Proceedings of the National Academy of Sciences of the United States of America 2026 · PMID 42378284 · doi:10.1073/pnas.2602385123

This dataset

Open GSE310452 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE310452_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 PRJNA1366361 and SRA study SRP647622. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42378284) ↗

Study design

12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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

GSM9301494 scMultiome_D8_GFPlo_ATAC
GSM9301495 scMultiome_D8_GFPhi_ATAC
GSM9301496 scMultiome_D30_GFPlo_ATAC
GSM9301497 scMultiome_D30_GFPhi_ATAC
GSM9301498 scMultiome_WT_ATAC
GSM9301499 scMultiome_Adora2ako_ATAC
GSM9301500 scMultiome_D8_GFPlo_GEX
GSM9301501 scMultiome_D8_GFPhi_GEX
GSM9301502 scMultiome_D30_GFPlo_GEX
GSM9301503 scMultiome_D30_GFPhi_GEX
GSM9301504 scMultiome_WT_GEX
GSM9301505 scMultiome_Adora2ako_GEX

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.