Antigen experience history directs distinct functional states of CD8+ CAR T cells during the anti-leukemia response
ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 96 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
- 96 samples: 72 distinct titles, 8 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
- Antigen experience history directs distinct functional states of CD8(+) CAR T cells during the antileukemia responseNature immunology · 2025
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 GSE249734_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 PRJNA1050258 and SRA study SRP476725. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 96 sample titles: 72 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.
- GSM7962170 Naïve, PreCAR, Exp1, TR1 [RNA-seq]
- GSM7962171 Naïve, PreCAR, Exp1, TR2 [RNA-seq]
- GSM7962172 Naïve, PreCAR, Exp1, TR3 [RNA-seq]
- GSM7962173 Memory, PreCAR, Exp1, TR1 [RNA-seq]
- GSM7962174 Memory, PreCAR, Exp1, TR2 [RNA-seq]
- GSM7962175 Memory, PreCAR, Exp1, TR3 [RNA-seq]
- GSM7962176 Naïve, PostCAR, Exp1, TR1 [RNA-seq]
- GSM7962177 Naïve, PostCAR, Exp1, TR2 [RNA-seq]
- GSM7962178 Naïve, PostCAR, Exp1, TR3 [RNA-seq]
- GSM7962179 Memory, PostCAR, Exp1, TR1 [RNA-seq]
- GSM7962180 Memory, PostCAR, Exp1, TR2 [RNA-seq]
- GSM7962181 Memory, PostCAR, Exp1, TR3 [RNA-seq]
- GSM7962182 Naïve, Tumor, Exp1, TR1 [RNA-seq]
- GSM7962183 Naïve, Tumor, Exp1, TR2 [RNA-seq]
- GSM7962184 Naïve, Tumor, Exp1, TR3 [RNA-seq]
- GSM7962185 Memory, Tumor, Exp1, TR1 [RNA-seq]
- GSM7962186 Memory, Tumor, Exp1, TR2 [RNA-seq]
- GSM7962187 Memory, Tumor, Exp1, TR3 [RNA-seq]
- GSM7962188 Naïve, PreCAR, Exp2, TR1 [RNA-seq]
- GSM7962189 Naïve, PreCAR, Exp2, TR2 [RNA-seq]
- GSM7962190 Memory, PreCAR, Exp2, TR1 [RNA-seq]
- GSM7962191 Memory, PreCAR, Exp2, TR2 [RNA-seq]
- GSM7962192 Naïve, PostCAR, Exp2, TR1 [RNA-seq]
- GSM7962193 Naïve, PostCAR, Exp2, TR2 [RNA-seq]
- GSM7962194 Memory, PostCAR, Exp2, TR1 [RNA-seq]
- GSM7962195 Memory, PostCAR, Exp2, TR2 [RNA-seq]
- GSM7962196 Naïve, Tumor, Exp2, TR1 [RNA-seq]
- GSM7962197 Naïve, Tumor, Exp2, TR2 [RNA-seq]
- GSM7962198 Memory, Tumor, Exp2, TR1 [RNA-seq]
- GSM7962199 Memory, Tumor, Exp2, TR2 [RNA-seq]
- GSM7962200 Naïve, PreCAR, Exp1, TR1 [ATAC-seq]
- GSM7962201 Naïve, PreCAR, Exp1, TR2 [ATAC-seq]
- GSM7962202 Naïve, PreCAR, Exp1, TR3 [ATAC-seq]
- GSM7962203 Memory, PreCAR, Exp1, TR1 [ATAC-seq]
- GSM7962204 Memory, PreCAR, Exp1, TR2 [ATAC-seq]
- GSM7962205 Memory, PreCAR, Exp1, TR3 [ATAC-seq]
- GSM7962206 Naïve, PostCAR, Exp1, TR1 [ATAC-seq]
- GSM7962207 Naïve, PostCAR, Exp1, TR2 [ATAC-seq]
- GSM7962208 Naïve, PostCAR, Exp1, TR3 [ATAC-seq]
- GSM7962209 Memory, PostCAR, Exp1, TR1 [ATAC-seq]
+ 56 more — browse all 96 samples with per-sample file links →
- GSE157232 The CD8+ T cell tolerance checkpoint triggers a transcriptionally and epigenetically distinct differentiation state defined by protein translation defects 30 samples shares cd8, distinct
- GSE302682 Sleeping Beauty mutagenesis identifies BACH2 and other regulators promoting CD8+ T cell persistence and effector function under tumor-associated chronic antigen stimulation - RNAseq 66 samples shares cd8, antigen
- GSE342312 T-bet and Runx3 orchestrate effector CD8 T cell differentiation and lineage fidelity through cooperative and distinct chromatin regulatory mechanisms [Multi-omics] 40 samples shares cd8, distinct
- GSE342096 Distinct activation programs in naïve and memory CD8 T cells govern progeny stemness and effector persistence 37 samples shares cd8, distinct
- GSE255887 HDAC1 controls the generation and maintenance of effector-like CD8+ T cells during chronic viral infection 76 samples shares cd8
- GSE248586 A conserved epithelial wound-like transcriptional program driven by FOSL1 underlies malignant progression of pancreatic ductal adenocarcinoma 57 samples shares car
- GSE267452 Loss of Nuclear Receptor Nr4a3 Alleviates Cellular Senescence to Promote Adult Direct Cardiac Reprogramming 54 samples shares car
- GSE301501 Extracellular matrix reprogramming by the YAP/TAZ/TGF-ß2 axis drives immune exclusion in cholangiocarcinoma models 45 samples shares car
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.