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A genome-scale screen for synthetic drivers of T-cell proliferation

GSE193736 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 44 samples Submitted 2022/01/21 Platform GPL21222Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 44 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
44 samples
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)
The engineering of patient T-cells for adoptive cell therapies has revolutionised the treatment of several cancer types. However, further improvements are needed to increase response and cure rates. CRISPR-based loss-of-function screens have been limited to negative regulators of T-cell functions and raise safety concerns due to permanent genome modification.
Read the full summary ↓
Here we identify positive T-cell regulators via overexpression of ~12,000 barcoded human open reading frames (ORFs). The top-ranked genes increased primary human CD4+ and CD8+ T-cell proliferation, activation, and secretion of key cytokines like interleukin-2 and interferon-gamma. In addition, we developed a single-cell genomics method (OverCITE-seq) for high-throughput quantification of the transcriptome and surface antigens in ORF-engineered T-cells. The top-ranked ORF, lymphotoxin beta receptor (LTBR), is typically expressed in myeloid cells but absent in lymphocytes. When overexpressed in T-cells, LTBR induced profound transcriptional and epigenomic remodelling, increasing T-cell effector functions, as well as resistance to exhaustion in chronic stimulation settings, via constitutive activation of the canonical NF-kappaB pathway. LTBR and other top-ranked genes improved antigen-specific chimeric antigen receptor (CAR) T-cell and gamma delta T-cell responses, highlighting their potential for future cancer-agnostic therapies5. Our results provide several strategies for improving next generation T-cell therapies via induction of new synthetic cell programmes.
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 GSE193736_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 PRJNA797432 and SRA study SRP355117. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 44 more — browse all 44 samples with per-sample file links →

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