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Analysis of (MC38) tumor infiltrating T regulatory cells From ERT2Cre control and ERT2CreTIM-3(FLEX4) KO mouse.

GSE293780 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/08/13 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 6 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)
Increased activity or number of regulatory T cells (Treg) serves as a barrier to effective anti-tumor immunity. Although manipulation of Treg cells is a promising anticancer strategy, doing so while sparing general immune tolerance has been a challenge. Thus, identifying factors specifically expressed in tumor-infiltrating Treg is important for better understanding cancer pathogenesis and identifying novel therapeutic targets that enhance anti-tumor immunity.
Read the full summary ↓
We show that Tim-3 expression on tumor Treg is required for the function and survival of these cells, in part through Akt activation and FOXO1 inactivation. Knocking out Tim-3 in Treg also leads to delayed CD8 T cell exhaustion and lower tumor burden, without altering peripheral homeostasis. Thus, Tim3-expressing Treg are a promising target to modulate tumor-specific immune responses.
This dataset
Open GSE293780 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 GSE293780_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 PRJNA1246209 and SRA study SRP576398. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
GFP+CD25+CD4+ cells from Cre control MC38 tum… ×2 GFP+CD25+CD4+ cells from Cre control MC38 tum… ×2 GFP+CD25+CD4+ cells from Cre control MC38 tum… ×2 GFP+CD25+CD4+ cells from Cre+FLEX4(Tim-3 KO) … ×2 GFP+CD25+CD4+ cells from Cre+FLEX4(Tim-3 KO) … ×2 GFP+CD25+CD4+ cells from Cre+FLEX4(Tim-3 KO) … ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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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