The Transcription Factor IRF8 drives tumor-specific exhaustion in CD8 T cells
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: 10 distinct titles, 1 of them repeatedGroups 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
- The transcription factor IRF8 drives tumor-specific exhaustion in CD8+ T cellsThe Journal of experimental medicine · 2026
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE333230_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1470529 and SRA study SRP703655. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 12 sample titles: 10 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.
- GSM9759599 Control IRF2 or IRF8_batch1
- GSM9759600 KO IRF2 or IRF8_batch1
- GSM9759601 Control IRF2 or IRF8_Hashtag_batch1
- GSM9759602 KO IRF2 or IRF8_Hashtag_batch1
- GSM9759603 Control IRF4 or IRF8_batch2
- GSM9759604 KO IRF4 or IRF8_batch2
- GSM9759605 Control IRF4 or IRF8_Hashtag_batch2
- GSM9759606 KO IRF4 or IRF8_Hashtag_batch2
- GSM9759607 IgG
- GSM9759608 IRF2
- GSM9759609 IRF4
- GSM9759610 IRF8
- GSE269676 3D genome of CD8+ T cells reveals IRF8-mediated exhaustion in cancer 18 samples shares irf8, cd8, exhaustion
- GSE223164 Osr2 functions as a biomechanical checkpoint to aggravate CD8+ T cell exhaustion in tumor 49 samples shares cd8, exhaustion
- GSE310452 Sustained A2AR expression and loss paradoxically promote CD8+ T cell exhaustion 12 samples shares cd8, exhaustion
- GSE307051 Active aldehydes accelerate CD8+ T cell exhaustion by metabolic alteration in the tumor microenvironment 3 samples shares cd8, exhaustion
- GSE243165 Signal-responsive transcription factors cooperate to establish alveolar macrophage identity 42 samples shares transcription, factor
- GSE281941 Klf5 functions as a multi-lineage reprogramming transcription factor 30 samples shares transcription, factor
- GSE286046 Asymmetric Histone Inheritance Regulates Differential Transcription Re-initiation and Cell Fate Decisions in Mouse Olfactory Horizontal Basal Cells 96 samples shares transcription, factor
- GSE281037 Large-scale transcriptomic analyses reveal downstream target genes of ZFY1 and ZFY2 transcription factors in male germ cells 48 samples shares transcription, factor
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.