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Dietary methionine restriction primes T cell metabolism for activation and tumor inhibition and enhances the efficacy of immune checkpoint blockade

GSE310218 Mus musculus (mouse) RNA-seq (gene expression) 21 samples Submitted 2026/05/20 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 21 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
21 samples: 6 conditions, mostly with about 4 replicates eachGroups 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 proliferation of many cancer cells is methionine dependent and dietary methionine restriction (MR) has shown anti-tumor effects in a wide variety of immunodeficiency preclinical models. Yet, whether MR exerts an anti-tumor effect in the presence of an immune-competent background remains inconclusive. Accumulating evidence has shown an essential role of methionine in immune cell differentiation and function.
Read the full summary ↓
Thus, competition for methionine between tumor cells and immune cells in the tumor microenvironment may drive tumor growth and tumor response to therapy. Here, we aim to define the impact of MR on tumor growth and associated immunity. We first assessed the effect of MR in a series of immunocompetent mouse models of melanoma, colorectal cancer, breast cancer, and lung. MR led to a broad tumor inhibition effect across these models and such tumor inhibition was not sex- or genetic background-dependent but appears to be fully or partially immune-dependent. Through flow cytometry analysis, we found a consistent increase in intratumoral activated CD8+ T cells across different tumor models and depletion of CD8+ T cells partially or completely reversed MR-induced tumor inhibition in a model dependent manner. Interestingly in young healthy non-tumor-bearing mice, MR increased spleen CD3+ and CD8+ T cell populations. Metabolomics and RNAseq analysis of spleen-derived CD8+ T cells revealed significant increase in purine metabolism and amino acid metabolism and that are in line with the metabolic feature of activated T cells. Furthermore, MR improved the efficacy of anti-PD1 immune checkpoint blockade. Together, MR primes T cell metabolism for its anti-tumor effect and improves the efficacy of anti-PD1 checkpoint blockade.
This dataset
Open GSE310218 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 GSE310218_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 PRJNA1365318 and SRA study SRP645537. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly with about 4 replicates each
MC38 model tumor cells, Con ×4 MC38 model tumor cells, M ×4 BPD6 model tumor cells, Con ×4 BPD6 model tumor cells, M ×3 CD8 Tcell_Con ×3 CD8 Tcell_M ×3

Supports a between-group comparison across 21 samples.

How we read this: 6 replicated groups read from 21 sample titles; they account for 21 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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