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Precision CRISPR annotation of the functional enhancer landscape in primary human T cells

GSE310188 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 11 samples Submitted 2026/01/01 Platform GPL21697
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 11 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
11 samples: 10 distinct titles, 1 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)
Precise modulation of T cell function through engineering the non-coding genome holds great promise for advancing next-generation immunotherapies. However, robust high-throughput approaches to annotate functional cis-regulatory elements (CRE) in human T cells remain limited. Here, we developed a simple and highly efficient CRISPR interference (CRISPRi) perturbation platform to systematically annotate CREs in human primary T cells.
Read the full summary ↓
Using this platform, we identified novel CREs controlling PDCD1, HAVCR2, and TBX21 expression. Combinatorial CRE perturbations revealed synergistic CRE pairs that fine-tune PDCD1 and HAVCR2 expression, while Cas9-indel-based mutagenesis pinpointed the critical nucleotides within each enhancer that are essential for their activity. Functional experiments demonstrated that CRE-edited HAVCR2 outperformed conventional total gene knockout in enhancing CAR T cells anti-tumor efficacy. Moreover, CRE editing of PDCD1 and HAVCR2 repressed PD-1 and TIM-3 expression in human tumor-infiltrating lymphocyte CD8 T cells, highlighting their regulatory role in disease relevant exhausted T cells. Together, this approach offers a compact CRISPRi platform that enables high-throughput dissection of functionally relevant genomic regions in T cells, providing insights for mechanistic studies and precision genome engineering for advanced cellular therapies.
This dataset
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.

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 GSE310188_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1365252 and SRA study SRP645456. Searching any of these in the dataset finder brings you back here.

Study design
10 distinct titles, 1 of them repeated
EM ×2

How we read this: Read from 11 sample titles: 10 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
Datasets on a similar topic

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