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Multimodal delineation of a layer of effector function among exhausted CD8 T cells in tumors

GSE295725 Homo sapiens (human) Single-cell RNA-seq 98 samples Submitted 2025/04/28 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 98 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
98 samples: 95 distinct titles, 3 of them repeatedGroups 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.
Paper
Multimodal delineation of a layer of effector function among exhausted CD8 T cells in tumorsScience immunology · 2025
Summary (written by the submitters)
The anti-tumor function of CD8 T cells is limited through well-established pathways of T cell exhaustion (TEX). Strategies to capture emergent functional states amongst this dominant trajectory of dysfunction are necessary to find pathways to durable anti-tumor immunity. By leveraging transcriptional reporting (by the fluorescent protein TFP) of the T cell activation marker Cd69, related to upstream AP-1 transcription factors, we define a classifier for potent versus sub-optimal CD69+ activation states arising from T cell stimulation.
Read the full summary ↓
In tumors, this delineation acts an additional functional readout along the TEX differentiation trajectory, within and across TEX subsets, marked by enhanced effector cytokine and granzyme B production. The more potent state remains differentially prominent in a T cell-mediated tumor clearance model, where they also show increased engagement in the microenvironment and are superior in tumor cell killing. Employing multimodal CITE-Seq in human head and neck tumors enables a similar strategy to identify Cd69RNAhiCD69+ cells that also have enhanced functional features in comparison to Cd69RNAloCD69+ cells, again within and across intratumoral CD8 T cell subsets. Refining the contours of the T cell functional landscape in tumors in this way paves the way for the identification of rare exceptional effectors, with imminent relevance to cancer treatment.
Published in
Multimodal delineation of a layer of effector function among exhausted CD8 T cells in tumors
Ray A, Bassette M, Hu KH et al. · Science immunology 2025 · PMID 40644511 · doi:10.1126/sciimmunol.adt3537
This dataset
Open GSE295725 in finder → Find more human 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 GSE295725_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 PRJNA1256256 and SRA study SRP581912. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40644511) ↗
Study design
95 distinct titles, 3 of them repeated
IPIBLAD005.T1.rna.tcell ×2 IPIPDAC006.T1.rna.tcell ×2 IPIMEL085.T1.rna.tcell ×2

How we read this: Read from 98 sample titles: 95 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

+ 58 more — browse all 98 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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