GEO series

Mettl3-dependent m6A modification is essential for effector differentiation and memory formation of CD8+ T cells [RNA-seq]

GSE215759 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2024/06/01 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 15 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: 15 samples: 6 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.

Summary (written by the submitters)

The goal of this study is to determine the critical requirement of N6-Methyladenosine (m6A) methyltransferase Mettl3 during CD8+ T cell responses upon acute viral infection. We demonstrate that conditional ablation of Mettl3 in CD8+ T cells impairs effector expansion and terminal differentiation in an m6A-dependent manner, which in turn affects memory formation and secondary response.

Read the full summary ↓

Mechanism analysis indicates that Mettl3 deficiency broadly affects expression of cell cycle and transcriptional regulators. Based on these findings, our study reveals the important role of m6A modification in CD8+ T cell responses.

This dataset

Open GSE215759 in finder → Find more mouse RNA-seq 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 GSE215759_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: 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 PRJNA890575 and SRA study SRP402639. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

RNA-seq-Tn-WT ×3 RNA-seq-Tn-KO ×3 RNA-seq-Tm-KO ×3 RNA-seq-Te-WT ×2 RNA-seq-Te-KO ×2 RNA-seq-Tm-WT ×2

Supports a between-group comparison across 15 samples.

How we read this: 6 replicated groups read from 15 sample titles; they account for 15 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.

Samples in this study

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

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