GEO series

Deletion of a state-specific PD-1 enhancer modulates exhausted T cell fate and function [RNA-Seq]

GSE269580 Mus musculus (mouse) Single-cell RNA-seq 16 samples Submitted 2024/07/29 Platform GPL32159

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. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 16 samples: 4 × WT CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample vs 4 × EnhDel CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample vs 4 × EnhDel CD8+ T cells, Day 9, Slamf6+TIM3-, sample vs 4 × WT CD8+ T cells, Day 9, Slamf6+TIM3-, sampleGroups 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: Epigenetic tuning of PD-1 expression improves exhausted T cell function and viral controlNature immunology · 2024

Summary (written by the submitters)

T cell exhaustion is a state of CD8+ T cell dysfunction elicited by chronic exposure to antigen and inflammation, arises in both cancer and chronic viral infection. The co-inhibitory receptor PD-1 plays a key role in mediating exhaustion, but complete ablation of PD-1 by gene knock-out leads to deeper functional deficits and poor T cell survival.

Read the full summary ↓

We hypothesized that an intermediate level of PD-1 expression may confer an improved balance of exhausted CD8+ T cell functionality, so we deleted an exhaustion-associated enhancer of PD-1 which indeed resulted in a reduced expression level. We compared EnhDel, WT and PD-1 KO T cells using single-cell RNA-Seq and found that PD-1 KO but not EnhDel cells are strongly biased towards the terminally exhausted subset. EnhDel cells also uniquely enrich for effector-associated genes and gene signatures. However, all three genotypes (EnhDel, WT and PD-1 KO) exhibit a similar chromatin accessibility landscape by ATAC-Seq, controlling for exhausted subset. These data suggest that tuning of PD-1 expression may uniquely permit the maintenance of an “effector” transcriptional profile in exhausted CD8+ T cells.

Published in

Epigenetic tuning of PD-1 expression improves exhausted T cell function and viral control

Weiss SA, Huang AY, Fung ME et al. · Nature immunology 2024 · PMID 39289557 · doi:10.1038/s41590-024-01961-3

This dataset

Open GSE269580 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 GSE269580_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 PRJNA1122563 and SRA study SRP513210. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39289557) ↗

Study design

4 × WT CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample vs 4 × EnhDel CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample vs 4 × EnhDel CD8+ T cells, Day 9, Slamf6+TIM3-, sample vs 4 × WT CD8+ T cells, Day 9, Slamf6+TIM3-, sample

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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

GSM8322427 WT CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample 3 WT CD8+ T cells, Day 9,…
GSM8322428 EnhDel CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample 6 EnhDel CD8+ T cells, Da…
GSM8322429 EnhDel CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample 12 EnhDel CD8+ T cells, Da…
GSM8322430 EnhDel CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample 18 EnhDel CD8+ T cells, Da…
GSM8322431 WT CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample 15 WT CD8+ T cells, Day 9,…
GSM8322432 WT CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample 21 WT CD8+ T cells, Day 9,…
GSM8322433 EnhDel CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample 25 EnhDel CD8+ T cells, Da…
GSM8322434 WT CD8+ T cells, Day 9, Slamf6-TIM3+CX3CR1-, sample 9 WT CD8+ T cells, Day 9,…
GSM8322435 EnhDel CD8+ T cells, Day 9, Slamf6+TIM3-, sample 4 EnhDel CD8+ T cells, Da…
GSM8322436 WT CD8+ T cells, Day 9, Slamf6+TIM3-, sample 1 WT CD8+ T cells, Day 9,…
GSM8322437 WT CD8+ T cells, Day 9, Slamf6+TIM3-, sample 7 WT CD8+ T cells, Day 9,…
GSM8322438 EnhDel CD8+ T cells, Day 9, Slamf6+TIM3-, sample 10 EnhDel CD8+ T cells, Da…
GSM8322439 EnhDel CD8+ T cells, Day 9, Slamf6+TIM3-, sample 16 EnhDel CD8+ T cells, Da…
GSM8322440 WT CD8+ T cells, Day 9, Slamf6+TIM3-, sample 13 WT CD8+ T cells, Day 9,…
GSM8322441 WT CD8+ T cells, Day 9, Slamf6+TIM3-, sample 19 WT CD8+ T cells, Day 9,…
GSM8322442 EnhDel CD8+ T cells, Day 9, Slamf6+TIM3-, sample 22 EnhDel CD8+ T cells, Da…

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