Mettl3-dependent m6A modification is essential for effector differentiation and memory formation of CD8+ T cells
RNA-seq (gene expression) · mouse · 41 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
- 41 samples: 16 conditions, mostly in triplicateGroups 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.
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 GSE215762_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 PRJNA890571 and SRA study SRP402639. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 41 samples.
How we read this: 16 replicated groups read from 41 sample titles; they account for 41 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.
- GSM6638255 RNA-seq-Tn-WT1 RNA-seq-Tn-WT
- GSM6638256 RNA-seq-Tn-WT2 RNA-seq-Tn-WT
- GSM6638257 RNA-seq-Tn-WT3 RNA-seq-Tn-WT
- GSM6638258 RNA-seq-Tn-KO1 RNA-seq-Tn-KO
- GSM6638259 RNA-seq-Tn-KO2 RNA-seq-Tn-KO
- GSM6638260 RNA-seq-Tn-KO3 RNA-seq-Tn-KO
- GSM6638261 RNA-seq-Te-WT1 RNA-seq-Te-WT
- GSM6638262 RNA-seq-Te-WT2 RNA-seq-Te-WT
- GSM6638263 RNA-seq-Te-KO1 RNA-seq-Te-KO
- GSM6638264 RNA-seq-Te-KO2 RNA-seq-Te-KO
- GSM6638265 RNA-seq-Tm-WT1 RNA-seq-Tm-WT
- GSM6638266 RNA-seq-Tm-WT2 RNA-seq-Tm-WT
- GSM6638267 RNA-seq-Tm-KO1 RNA-seq-Tm-KO
- GSM6638268 RNA-seq-Tm-KO2 RNA-seq-Tm-KO
- GSM6638269 RNA-seq-Tm-KO3 RNA-seq-Tm-KO
- GSM6638270 miCLIP-seq-Input-WT1 miCLIP-seq-Input-WT
- GSM6638271 miCLIP-seq-Input-WT2 miCLIP-seq-Input-WT
- GSM6638272 miCLIP-seq-Input-KO1 miCLIP-seq-Input-KO
- GSM6638273 miCLIP-seq-Input-KO2 miCLIP-seq-Input-KO
- GSM6638274 miCLIP-seq-IP-WT1 miCLIP-seq-IP-WT
- GSM6638275 miCLIP-seq-IP-WT2 miCLIP-seq-IP-WT
- GSM6638276 miCLIP-seq-IP-KO1 miCLIP-seq-IP-KO
- GSM6638277 miCLIP-seq-IP-KO2 miCLIP-seq-IP-KO
- GSM6638278 input-effector-rep1 input-effector
- GSM6638279 input-effector-rep2 input-effector
- GSM6638280 input-effector-rep3 input-effector
- GSM6638281 input-memory-rep1 input-memory
- GSM6638282 input-memory-rep2 input-memory
- GSM6638283 input-memory-rep3 input-memory
- GSM6638284 input-Naive-rep1 input-Naive
- GSM6638285 input-Naive-rep2 input-Naive
- GSM6638286 input-Naive-rep3 input-Naive
- GSM6638287 IP-effector-rep1 IP-effector
- GSM6638288 IP-effector-rep2 IP-effector
- GSM6638289 IP-effector-rep3 IP-effector
- GSM6638290 IP-memory-rep1 IP-memory
- GSM6638291 IP-memory-rep2 IP-memory
- GSM6638292 IP-memory-rep3 IP-memory
- GSM6638293 IP-Naive-rep1 IP-Naive
- GSM6638294 IP-Naive-rep2 IP-Naive
+ 1 more — browse all 41 samples with per-sample file links →
- GSE215759 Mettl3-dependent m6A modification is essential for effector differentiation and memory formation of CD8+ T cells [RNA-seq] 15 samples shares cd8, mettl3-dependent, modification, essential, formation, effector, memory
- GSE317032 Non-cytolytic viral vaccine vectors induce durable effector-memory CD8 T cell immunity by triggering type I interferon 18 samples shares cd8, effector, memory
- GSE261250 Gfi1 controls the formation of effector CD8+ T cells during chronic infection and cancer 47 samples shares cd8, formation, effector
- GSE342096 Distinct activation programs in naïve and memory CD8 T cells govern progeny stemness and effector persistence 37 samples shares cd8, effector, memory
- GSE306744 Pre-division TCF1 downregulation determines long-term effector and memory CD8 T cell fates [RNA-Seq] 31 samples shares cd8, effector, memory
- 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, effector
- GSE281498 The transcription factor BATF pioneers the differentiation program of cytolytic effector CD8+ T cells through the direct interaction with IRF4 (ATAC-seq) 20 samples shares cd8, effector
- GSE286266 Age influences CMV-specific NK and CD8 T cell memory fates (NK cells) 12 samples shares cd8, memory
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.