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Eomes sustains progenitor CD4⁺ T helper cells to promote anti-tumor immunity during immunotherapy [scRNA]

GSE324687 Mus musculus (mouse) Single-cell RNA-seq 16 samples Submitted 2026/06/01 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 16 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
16 samples: 16 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)
CD4⁺ T helper (Th) cells contribute to tumor immunity, yet the subsets and differentiation programs involved remain unclear. Here, we show that the transcription factor Eomesodermin (Eomes) is essential for Th–mediated anti-tumor immunity by orchestrating the differentiation and maintenance of an exhausted-like Th cell lineage, which is transcriptionally and functionally distinct from conventional effector or memory Th subsets.
Read the full summary ↓
This Eomes-dependent program is further enhanced by 4-1BB stimulation, promoting effective Th cell–mediated tumor control. The precursor subset of this lineage (pTh) expresses stemness-associated transcription factors, displays self-renewal capacity, and can differentiate into effector cells capable of controlling tumor growth. At the transcriptional level, Eomes supports the survival, metabolic fitness, and apoptotic resistance of this lineage. Notably, Eomes⁺ pTh cells are directly relevant to human cancer, being detected across multiple tumor types, exhibiting conserved transcriptional features, and representing the most expanded Th cell population upon immune checkpoint blockade therapy.
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 GSE324687_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 PRJNA1436567 and SRA study SRP683207. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 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 16 sample titles: 16 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
Datasets on a similar topic

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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.