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LAG-3 sustains TOX and regulates the CD94/NKG2-Qa-1b axis to govern exhausted CD8 T cell NK receptor expression and cytotoxicity

GSE271184 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2025/03/01 Platform GPL21626
At a glance

RNA-seq (gene expression) · mouse · 16 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
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.
Paper
LAG-3 sustains TOX expression and regulates the CD94/NKG2-Qa-1b axis to govern exhausted CD8 T cell NK receptor expression and cytotoxicityCell · 2024
Summary (written by the submitters)
Exhausted CD8 T cells (Tex) found in chronic viral infection and cancer are defined by sustained co-expression of inhibitory receptors (IRs). Tex can be reinvigorated by blocking IRs, such as PD-1, but synergistic reinvigoration of Tex and enhanced disease control can be achieved by co-targeting multiple IRs including co-blockade of PD-1 and LAG-3, a treatment recently FDA-approved for melanoma.
Read the full summary ↓
Despite the promise of anti-PD-1/anti-LAG-3 therapy, the cellular and molecular changes intrinsic to CD8 T cells when one or more of these IR pathways are disrupted remain poorly understood. To dissect these IR pathways and their interactions with each other, we used a quadruple co-transfer model to investigate the impact of singular or combined loss of PD-1 and LAG-3 on the differentiation of Tex during chronic viral infection. These analyses revealed distinct roles of PD-1 and LAG-3 in regulating Tex proliferation and cytokine/cytotoxic functions, respectively. Moreover, these studies identified key roles for LAG-3 that complement PD-1 biology including an essential role for LAG-3 in sustaining TOX expression and associated Tex durability. Finally, these studies uncovered a LAG-3-dependent circuit for the generation of an effector-like CD94/NKG2+ subset of Tex capable of enhanced cytotoxicity mediated by recognition of the stress-induced ligand Qa-1b with similar regulation of CD94/NKG2 family members in humans with combination anti-PD-1/anti-LAG-3. These analyses identify a role for LAG-3 in governing CD8 T cell Qa-1b-restricted immune sensing and surveillance. Together, these results disentangled the underlying non-redundant mechanisms of PD-1 and LAG-3 and their synergy in regulating Tex revealing additional immunotherapeutic opportunities based on these pathways.
Published in
LAG-3 sustains TOX expression and regulates the CD94/NKG2-Qa-1b axis to govern exhausted CD8 T cell NK receptor expression and cytotoxicity
Ngiow SF, Manne S, Huang YJ et al. · Cell 2024 · PMID 39121847 · doi:10.1016/j.cell.2024.07.018
This dataset
Open GSE271184 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 GSE271184_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1130418 and SRA study SRP517253. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39121847) ↗
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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