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Krüppel-like factor 2 programs early exhausted T cell states and restrains antiviral immunity [RNA-Seq]

GSE288114 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2026/04/18 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 32 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
32 samples: 10 conditions, mostly in triplicateGroups 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
Krüppel-like factor 2 programs early exhausted T cell states and restrains antiviral immunityImmunity · 2026
Summary (written by the submitters)
A key challenge in improving T cell-mediated immunotherapies is defining the factors that regulate functional versus exhaustion T cell fates. Through multi-round in vivo CRISPR screens in chronic LCMV Clone 13 infection and transcription factor (TF) benchmarking, we identified KLF2 as a top TF driving CX3CR1⁺ effector-like exhausted cell (Texeff-like) differentiation.
Read the full summary ↓
Overexpression of KLF2 converted CX3CR1⁻ cells into Texeff-like cells by direct engagement of key loci. Conversely, loss of KLF2 increased inhibitory receptor expression and redirected cells toward terminal exhaustion. However, early after infection, KLF2 deficiency yielded increased CD8+ T cell accumulation and improved viral control. This effect was, in part, mediated by TOX and improved T cell localization with dendritic cells. Additional deletion of PD-1 further enhanced viral control, but induced severe immunopathology. Collectively, these findings identify KLF2 as a central regulator of the Texeff-like program and underscore exhaustion features as checkpoints balancing antiviral immunity and immunopathology.
Published in
Krüppel-like factor 2 programs early exhausted T cell states and restrains antiviral immunity
Geng S, Li Z, Li W et al. · Immunity 2026 · PMID 42049034 · doi:10.1016/j.immuni.2026.03.029
This dataset
Open GSE288114 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 GSE288114_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 PRJNA1216216 and SRA study SRP560132. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42049034) ↗
Study design
10 conditions, mostly in triplicate
RNA-seq_sgNTC_P14_Cl13_D5.5 ×4 RNA-seq_sgKlf2_P14_Cl13_D5.5 ×4 RNA-seq_pMIG_in_vitro ×3 RNA-seq_pMIG-Klf2_in_vitro ×3 RNA-seq_Texeff-es-like_P14_Cl13_D7.5 ×3 RNA-seq_Texterm-es_P14_Cl13_D7.5 ×3 RNA-seq_rep_sgNTC_P14_Cl13_D5.5 ×3 RNA-seq_rep_sgKlf2_P14_Cl13_D5.5 ×3 +2 more

Supports a between-group comparison across 32 samples.

How we read this: 10 replicated groups read from 32 sample titles; they account for 32 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
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