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Genome-scale perturb-seq in primary human CD4+ T cells reveals genes regulating T cell programs and human immune traits.

GSE314342 Homo sapiens (human) RNA-seq (gene expression) 577 samples Submitted 2026/02/06 Platform GPL34762Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 577 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
Homo sapiens (human)
Samples
577 samples: 329 distinct titles, 4 of them repeatedGroups 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)
CD4+ T cells orchestrate immune responses against infection and cancer, yet a comprehensive map of their gene regulatory networks has been lacking. Perturb-seq enables systematic mapping of such networks, but large-scale application in primary human T cells has been challenging. Here, we developed a scalable, probe-based Perturb-seq platform and performed genome-scale perturbations of all expressed genes across 22 million primary CD4+ T cells from four donors under three stimulation conditions.
Read the full summary ↓
We demonstrate the utility of this dataset in three ways. First, we identify previously unrecognized regulators of the two important immune cytokines, IL-10 and IL-21. Second, we show that ex vivo perturbation signatures can model T cell states observed in population-scale atlases, revealing regulators of helper T cell polarization and T cell aging. Third, we leverage regulatory relationships to mechanistically explain gene–trait associations in immune-related diseases, nominating genes for uncharacterized immune functions. Thus genome-scale perturb-seq in primary human T cells provides a foundational resource for decoding immune variation in humans and reprogramming T cell states for next-generation precision therapies.
This dataset
Open GSE314342 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 GSE314342_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 PRJNA1359008 and SRA study SRP643211. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
329 distinct titles, 4 of them repeated
scRNA-seq CRISPR interference screen of CD4+,… ×47 scRNA-seq CRISPR interference screen of CD4+,… ×24 DonorC cells perturbed with Non-targeting con… ×2 DonorC cells perturbed with Non-targeting con… ×2

Supports a case/control comparison: 267 samples read as cases, 56 as controls.

How we read this: Read from the first 400 of 577 sample titles: 329 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

+ 537 more — browse all 577 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.