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PD-1 regulates tumor-infiltrating CD8+ T cells in both a cell-intrinsic and cell-extrinsic fashion [scRNA-seq]

GSE290839 Mus musculus (mouse) Single-cell RNA-seq 20 samples Submitted 2025/05/29 Platform GPL21626
At a glance

Single-cell RNA-seq · mouse · 20 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
20 samples: 6 conditions, mostly in duplicateGroups 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)
Although PD-1 inhibitors are FDA-approved for over 25 different cancers, the mechanisms contributing to response remain incompletely understood. To elucidate the impact of PD-1 deleted CD8+ T cells on PD-1-expressing CD8+ T cells in the same tumor microenvironment, we developed an inducible PD-1 knockout (KO) model in which PD-1 is deleted on approximately 50% of cells.
Read the full summary ↓
PD-1 deletion beginning at day 7 after implantation of MC38 tumor cells induced robust tumor control. Remarkably, PD-1-expressing CD8+ T cells in the tumor had increased functionality similar to PD-1 KO CD8+ T cells. Using single cell RNA seq and TCR seq, we found that the major transcriptional changes following PD-1 deletion were shared by PD-1 KO and PD-1-expressing CD8+ T cells, though PD-1 KO clones preferentially expanded. These data suggest PD-1 inhibitors not only exert cell-intrinsic effects but also may promote increased T cell function via non-cell autonomous mechanisms, which has important implications for design of PD-1-based cancer immunotherapies.
This dataset
Open GSE290839 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 GSE290839_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 PRJNA1230217 and SRA study SRP567137. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
feature barcoding library of Cre- CD8+ T cell… ×2 gene expression library of Cre- CD8+ T cells,… ×2 V(D)J library of Cre- CD8+ T cells, from MC38… ×2 feature barcoding library of Cre+ CD8+ T cell… ×2 gene expression library of Cre+ CD8+ T cells,… ×2 V(D)J library of Cre+ CD8+ T cells, from MC38… ×2 gene expression library of PD-1- sorted Cre+ … ×1 V(D)J library of PD-1- sorted Cre+ CD8+ T cel… ×1 +6 more

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 20 sample titles; they account for 12 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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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.