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A post-transcriptional regulatory checkpoint restrains acquisition of cytotoxic activity in tumor-infiltrating CD4+ T cells

GSE230230 Mus musculus (mouse) RNA-seq (gene expression) 13 samples Submitted 2024/12/19 Platform GPL19057Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 13 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
13 samples: 5 conditions, mostly in duplicateGroups 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)
Tumor-infiltrating cytotoxic CD4+ T cells (TCTX) are a promising therapeutic target, but how their activity is regulated is poorly understood. Here, we show that a post-transcriptional checkpoint restrains TCTX activity in suppressive tumor microenvironments. In untreated tumors, poised TCTX express high levels of Gzmb mRNA but produce little Granzyme B (GzmB) protein.
Read the full summary ↓
Differentiation into poised TCTX is regulated by the Blimp-1-Bcl6 axis and requires type I interferon signaling. GzmB protein is produced by tumor-infiltrating TCTX upon treatment with TREG-depleting aCTLA-4 antibodies and following blockade of LAG-3 and PD-1. These treatments downregulate Zfp36l1, encoding an RNA binding protein. Deletion of Zfp36l1 and its paralog Zfp36 increases GzmB protein in CD4+ T cells in untreated tumors and reduces tumor mass. These results demonstrate that TCTX activity is restrained in tumors through LAG-3 and PD-1 and the ZFP36 family of RNA binding proteins. We propose that these pathways represent targets for therapeutic intervention.
This dataset
Open GSE230230 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 GSE230230_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 PRJNA958099 and SRA study SRP433693. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 conditions, mostly in duplicate
TIL_WT_UT ×2 TIL_WT_aCTLA4 ×2 TIL_CD4Cre_UT ×2 TIL_Bcl6-/-_UT ×2 TIL_Prdm1-/-_UT ×2 TIL_CD4Cre_aCTLA-4 ×1 TIL_Bcl6-/-_aCTLA-4 ×1 TIL_Prdm1-/-_aCTLA-4 ×1

Supports a between-group comparison across 10 samples.

How we read this: 5 replicated groups read from 13 sample titles; they account for 10 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
Datasets on a similar topic

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