GEO series

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

GSE311149 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2025/12/01 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 2 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 2 samples
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

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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 GSE311149_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1368713 and SRA study SRP648456. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

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+ 2 more — browse all 2 samples with per-sample file links →

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