GEO series

An engineered viral protein activates STAT5 to prevent T cell suppression

GSE289168 Mus musculus (mouse) Single-cell RNA-seq 14 samples Submitted 2025/05/19 Platform GPL34290

At a glance

Single-cell RNA-seq · mouse · 14 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: 14 samples: 14 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: An engineered viral protein activates STAT5 to prevent T cell suppressionScience immunology · 2025

Summary (written by the submitters)

T cell therapy efficacy can be compromised if cytokine-induced Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling is dysregulated or insufficient to sustain functionality. Here, we demonstrate that LCK kinase activity can be recruited to non-canonical protein substrates to directly activate targeted STAT proteins in T cells.

Read the full summary ↓

STAT activation was accomplished by engineering the Herpesvirus saimiri (HVS) tyrosine kinase interacting protein (TIP) to provide a platform for the enforced recruitment of LCK to STAT proteins. We determined that a minimal region of TIP that binds to LCK could be combined with STAT binding sites derived from endogenous cytokine receptors. These constructs activated targeted STAT proteins in a cytokine-independent manner. We identified a STAT5 activator that sustained CD8+ T cell survival and cytotoxic function ex vivo in the absence of IL-2. Tumor outgrowth was reduced in vivo due to enhanced T cell persistence and functionality. Single-cell transcriptomics revealed the STAT5 activator prevented the expression of genes associated with an exhausted T cell fate. Our findings demonstrate that signaling pathways can be rewired in T cells to sustain their function in solid tumors.

Published in

An engineered viral protein activates STAT5 to prevent T cell suppression

Zheng Y, Gu Z, Shudde CE et al. · Science immunology 2025 · PMID 40408430 · doi:10.1126/sciimmunol.adn9633

This dataset

Open GSE289168 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 GSE289168_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 PRJNA1221848 and SRA study SRP563053. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40408430) ↗

Study design

14 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 14 sample titles: 14 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

GSM8785634 Multimodal, T cells, D5.aSTAT5 rep1-3, GEX
GSM8785635 Multimodal, T cells, D5.aSTAT5 rep 1-3, ADT
GSM8785636 Multimodal, T cells, D5.EmptyVec rep1-3, GEX
GSM8785637 Multimodal, T cells, D5. Empty Vec rep 1-3, ADT
GSM8785638 Multimodal, T cells, D5.STAT5CA rep1-3, GEX
GSM8785639 Multimodal, T cells, D5.STAT5CA rep 1-3, ADT
GSM8785640 Multimodal, T cells, D5.PepControl rep1-3, GEX
GSM8785641 Multimodal, T cells, D5.PepControl rep 1-3, ADT
GSM8785642 Multimodal, T cells, D12.EmptyVec rep1-3, GEX
GSM8785643 Multimodal, T cells, D12.EmptyVec rep 1-3, ADT
GSM8785644 Multimodal, T cells, D12.aSTAT5 rep1-3, GEX
GSM8785645 Multimodal, T cells, D12.aSTAT5 rep 1-3, ADT
GSM8785646 Multimodal, T cells, D12.STAT5CA rep1-3, GEX
GSM8785647 Multimodal, T cells, D12.STAT5CA rep 1-3, ADT

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