Deciphering the role of histone modifications in memory and exhausted CD8 T cells [RNA-seq]
RNA-seq (gene expression) · mouse · 16 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Mus musculus (mouse)
- Samples
- 16 samples: 4 × TN_Naive, RNA, Day30, vs 4 × TN_Naive, RNA, Day32, vs 4 × TMEM_Memory, RNA, Day30, vs 4 × TEX_Exhaustion, RNA, Day32,Groups 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
- Deciphering the role of histone modifications in memory and exhausted CD8 T cellsScientific reports · 2025
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 GSE285248_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1202106 and SRA study SRP553479. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 16 samples.
How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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.
- GSM8699189 TN_Naive, RNA, Day30, rep1 TN_Naive, RNA, Day30,
- GSM8699190 TN_Naive, RNA, Day30, rep2 TN_Naive, RNA, Day30,
- GSM8699191 TN_Naive, RNA, Day30, rep3 TN_Naive, RNA, Day30,
- GSM8699192 TN_Naive, RNA, Day30, rep4 TN_Naive, RNA, Day30,
- GSM8699193 TN_Naive, RNA, Day32, rep1 TN_Naive, RNA, Day32,
- GSM8699194 TN_Naive, RNA, Day32, rep2 TN_Naive, RNA, Day32,
- GSM8699195 TN_Naive, RNA, Day32, rep3 TN_Naive, RNA, Day32,
- GSM8699196 TN_Naive, RNA, Day32, rep4 TN_Naive, RNA, Day32,
- GSM8699197 TMEM_Memory, RNA, Day30, rep1 TMEM_Memory, RNA, Day30,
- GSM8699198 TMEM_Memory, RNA, Day30, rep2 TMEM_Memory, RNA, Day30,
- GSM8699199 TMEM_Memory, RNA, Day30, rep3 TMEM_Memory, RNA, Day30,
- GSM8699200 TMEM_Memory, RNA, Day30, rep4 TMEM_Memory, RNA, Day30,
- GSM8699201 TEX_Exhaustion, RNA, Day32, rep1 TEX_Exhaustion, RNA, Da…
- GSM8699202 TEX_Exhaustion, RNA, Day32, rep2 TEX_Exhaustion, RNA, Da…
- GSM8699203 TEX_Exhaustion, RNA, Day32, rep3 TEX_Exhaustion, RNA, Da…
- GSM8699204 TEX_Exhaustion, RNA, Day32, rep4 TEX_Exhaustion, RNA, Da…
- GSE337601 XCR1+ and CD11b+ migratory dendritic cells cooperate for the crosspriming of intratumoral CD8+ T cells with a tissue-resident memory phenotype [RNA-Seq] 36 samples shares cd8, rna-seq, memory
- GSE215759 Mettl3-dependent m6A modification is essential for effector differentiation and memory formation of CD8+ T cells [RNA-seq] 15 samples shares cd8, rna-seq, memory
- GSE306744 Pre-division TCF1 downregulation determines long-term effector and memory CD8 T cell fates [RNA-Seq] 31 samples shares cd8, rna-seq, memory
- GSE235005 Nutrient-driven histone code determines exhausted CD8+ T cell fates [ChIP-seq] 50 samples shares cd8, exhausted, histone
- GSE337898 ATAC-seq profiling of lineage-traced exhausted and memory-like CD8+ T cell subsets during tumor progression 24 samples shares cd8, exhausted, memory
- GSE290839 PD-1 regulates tumor-infiltrating CD8+ T cells in both a cell-intrinsic and cell-extrinsic fashion [scRNA-seq] 20 samples shares cd8, rna-seq
- GSE264650 RNA-seq of murine CD8+ tumor-infiltrating T cells following treatment with pembrolizumab and/or ABL503 20 samples shares cd8, rna-seq
- GSE269347 Daunorubicin-induced T cell remodeling facilitates a high antitumor response to PD-1 blockade therapy by promoting the expansion and effector function of CD8+ prolifeating T cells [scRNA-Seq] 16 samples shares cd8, rna-seq
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.