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Centralized control of dynamic gene regulatory circuits governs human T cell rest and activation [Bulk_RNA]

GSE276096 Homo sapiens (human) Single-cell RNA-seq 96 samples Submitted 2024/10/15 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 96 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
Homo sapiens (human)
Samples
96 samples: 96 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
Central control of dynamic gene circuits governs T cell rest and activationNature · 2025
Summary (written by the submitters)
The ability of individual cells to maintain a distinct identity and respond to transient environmental signals requires tightly controlled regulation of gene networks. However, how discrete sets of gene regulators coordinate circuits that respond to extracellular cues remains poorly defined. The need for context-dependent regulation is prominent in human CD4+ T cells, where distinct cell lineages must respond to diverse signals to orchestrate effective adaptive immune responses and maintain homeostasis.
Read the full summary ↓
Here, we performed CRISPR screens in multiple primary human CD4+ T cell contexts to identify regulators that control expression of IL2RA, which is a canonical marker of T cell activation in pro-inflammatory effector T cells (Teffs) and constitutively expressed in anti-inflammatory regulatory T cells (Tregs) where it is required for fitness. Strikingly, the majority of identified regulators are required in discrete cell type and stimulation timepoints, and a subset even had opposite functional effects in different conditions. Using single-cell transcriptomics after pooled perturbation of context-specific screen hits, we characterized specific factors as regulators of overall rest or activation and constructed state-specific regulatory networks. Upstream of these networks, MED12 – a component of the Mediator complex – serves as a dynamic orchestrator of regulators across conditions, governing both cell type- and stimulation-specific gene expression. MED12 interacts with the histone modifying complex COMPASS, affecting chromatin state and expression of genes encoding key context- and lineage-specific regulators; including rest maintenance factor KLF2 and activation promoting genes. We demonstrated that CRISPR ablation of MED12 blunted this cell state transition and protected T cells from activation-induced cell death. Overall, this work leverages CRISPR screens performed across primary cell conditions to identify context-specific regulators and dynamic gene circuits required to establish resting and activated T cell states.
Published in
Central control of dynamic gene circuits governs T cell rest and activation
Arce MM, Umhoefer JM, Arang N et al. · Nature 2025 · PMID 39663454 · doi:10.1038/s41586-024-08314-y
This dataset
Open GSE276096 in finder → Find more human 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 GSE276096_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 PRJNA1154679 and SRA study SRP529708. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39663454) ↗
Study design
96 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 96 sample titles: 96 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

+ 56 more — browse all 96 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.