GEO series

CD200+ CD8 T Cell Generated through Arginine Deprivation to Activate Macrophage Cross-presentation and Improve Anti-Tumor Immunity [RNA-Seq 2]

GSE311148 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2025/12/01 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 10 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: 10 samples: 10 samples, each with a distinct title — no replicated groupsGroups 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)

Arginine is indispensable for both tumor progression and the function of immune cells, yet how arginine deprivation within the tumor microenvironment shapes anti-tumor immunity has remained unclear. In this study, we established an in vivo arginine-restriction model using a diet lacking arginine, which significantly suppressed tumor growth and activated global anti-tumor immune responses.

Read the full summary ↓

Mechanistically, arginine deprivation triggered the integrated stress response in CD8+ T cells via the GCN2–cREL signaling axis, leading to elevated CD200 expression. These CD200+ CD8+ T cells in turn enhanced the antigen cross-presentation capacity of tumor-associated macrophages through CD200–CD200R interactions, forming a cooperative immune network that reinforced anti-tumor activity. Moreover, this arginine deprivation–induced cellular synergy improved the therapeutic efficacy of immunotherapy in colorectal cancer models. We further conducted a single-arm phase I clinical trial (NCT07038044), which demonstrated that an arginine-free diet is both feasible and safe for modulating systemic immunity in patients. Together, our findings provide a new conceptual framework for understanding the role of arginine in reshaping the tumor immune microenvironment and highlight arginine restriction as a promising and clinically translatable anti-tumor strategy.

This dataset

Open GSE311148 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 GSE311148_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 PRJNA1368757 and SRA study SRP648466. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

10 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 10 sample titles: 10 distinct titles with little repetition. 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

GSM9317704 Arg1_RNA
GSM9317706 Arg2_RNA
GSM9317708 Arg3_RNA
GSM9317710 Arg4_RNA
GSM9317712 Arg5_RNA
GSM9317714 Control1_RNA
GSM9317716 Control2_RNA
GSM9317718 Control3_RNA
GSM9317720 Control4_RNA
GSM9317722 Control5_RNA

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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.