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Expanding the cytokine receptor alphabet reprograms T cells into diverse cell states: RNAseq of orthogonal chimera transduced T cells

GSE282973 Mus musculus (mouse) RNA-seq (gene expression) 57 samples Submitted 2024/12/09 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 57 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
57 samples: 19 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
Expanding the cytokine receptor alphabet reprograms T cells into diverse statesNature · 2025
Summary (written by the submitters)
Cytokines signal through specific cell surface receptor dimeric pairings that have been selected over the course of evolution to activate canonical JAK/STAT signaling pathways and gene expression programs. However, the potential combinatorial diversity of JAK/STAT cytokine receptor pairings is much greater than what is utilized in nature, raising questions about the untapped biology of alternative 'non-natural' pairings.
Read the full summary ↓
Here we exploited common γ chain (γc) receptor as a shared signaling hub on T cells and enforced the expression of both natural and non-natural heterodimeric JAK/STAT receptor pairings using an orthogonal cytokine receptor platform, followed by a comparative analysis of the resulting T cell phenotypes in vivo. We tested receptors from γc cytokines as well as interferon, IL-10, and other homodimeric receptor families that do not normally pair with γc or are not naturally expressed on T cells. These synthetic receptors induced unique gene expression programs, and led to distinct T cell fates in tumors, including naturally occurring states (type 2 cytotoxic T (Tc2) cell and type 2 helper T (Th2) cell differentiation driven by orthogonal IL-4R) and synthetic states (myeloid-like phagocytic T cells driven by orthogonal GSCFR). T cells armed with orthogonal IL-22R (o22R) and oGCSFR exhibited transcriptional, and chromatin landscapes associated with stemness and resistance to exhaustion, which enhanced anti-tumor properties. Non-native JAK/STAT signals open a path to diversifying T-cell states beyond those induced by the menu of natural cytokines.
Published in
Expanding the cytokine receptor alphabet reprograms T cells into diverse states
Zhao Y, Ogishi M, Pal A et al. · Nature 2025 · PMID 40804519 · doi:10.1038/s41586-025-09393-1
This dataset
Open GSE282973 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 GSE282973_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 PRJNA1191395 and SRA study SRP548000. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40804519) ↗
Study design
19 conditions, mostly in triplicate
No-ICD + MSA-oIL2 ×3 o2R + MSA-oIL2 ×3 o2R + IL-2 ×3 o4R + MSA-oIL2 ×3 o4R + IL-4 ×3 o7R + MSA-oIL2 ×3 o7R + IL-7 ×3 o9R + MSA-oIL2 ×3 +11 more

Supports a between-group comparison across 57 samples.

How we read this: 19 replicated groups read from 57 sample titles; they account for 57 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

+ 17 more — browse all 57 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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