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Genetic and epigenetic screens in primary human T cells link candidate causal autoimmune variants to T cell networks [MPRA]

GSE297471 Homo sapiens (human) Single-cell RNA-seq 13 samples Submitted 2025/05/19 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 13 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
13 samples: 7 × Primary T MPRA mRNA vs 5 × MPRA plasmidGroups 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.
Summary (written by the submitters)
Genetic variants associated with autoimmune diseases are highly enriched within putative cis-regulatory regions of CD4+ T cells, suggesting that they alter disease risk via changes in gene regulation. However, very few genetic variants have been shown to affect T cell gene expression or function. Here, we tested >18,000 autoimmune disease-associated variants for allele-specific expression using massively parallel reporter assays in primary human CD4+ T cells.
Read the full summary ↓
We find 545 variants that modulate expression in an allele-specific manner (emVars). Primary T cell emVars greatly enrich for probable causal variants, are mediated by common upstream pathways, and their putative target genes are highly enriched within a lymphocyte activation network. Using bulk and single-cell CRISPR-interference screens, we confirm that emVar-containing T cell cis-regulatory elements modulate both known and previously unappreciated target genes that regulate T cell proliferation, providing plausible mechanisms by which these variants alter autoimmune disease risk.
This dataset
Open GSE297471 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 GSE297471_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 PRJNA1264965 and SRA study SRP586214. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 × Primary T MPRA mRNA vs 5 × MPRA plasmid

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 13 sample titles; they account for 12 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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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.