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Homology mediated end joining enables efficient non-viral targeted integration of large DNA templates in primary human T cells

GSE246558 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/11/04 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 18 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.
Summary (written by the submitters)
Adoptive cellular therapy using genetically engineered immune cells holds tremendous promise for the treatment of advanced cancers. While the number of available receptors targeting tumor specific antigens continues to grow, the current reliance on viral vectors for clinical production of engineered immune cells remains a substantial bottleneck limiting translation of promising new therapies.
Read the full summary ↓
Here, we describe an optimized methodology for efficient CRISPR-Cas9 based, non-viral engineering of primary human T cells that overcomes key limitations of previous approaches. By synergizing temporal optimization of reagent delivery, reagent composition, and integration mechanism, we achieve targeted integration of large DNA cargo at efficiencies nearing those of viral vector platforms with minimal toxicity. CAR-T cells generated using our approach are highly functional and elicit potent anti-tumor cytotoxicity in vitro and in vivo. Importantly, our method is readily adaptable to current Good Manufacturing Practices (cGMP) and clinical scale-up, offering a near-term alternative to the use of viral vectors for production of genetically engineered T cells for cancer immunotherapy.
This dataset
Open GSE246558 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 GSE246558_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 PRJNA1033634 and SRA study SRP469285. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 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 18 sample titles: 18 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

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.