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In vivo gene editing of human hematopoietic stem and progenitor cells using envelope-engineered virus-like particles [cell line panel]

GSE267496 Homo sapiens (human) RNA-seq (gene expression) 11 samples Submitted 2025/12/17 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 11 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
11 samples: 11 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.
Paper
In vivo gene editing of human hematopoietic stem and progenitor cells using envelope-engineered virus-like particlesNature biotechnology · 2025
Summary (written by the submitters)
Engineered virus-like particles (VLPs) are a promising technology for in vivo gene editing of human hematopoietic stem and progenitor cells (HSPCs). Here we design and test two different VLP envelopes for human HSPC editing in vitro and in vivo. The first is an optimized version of the baboon envelope BaEVTR, which efficiently transduces human HSPCs in vitro.
Read the full summary ↓
We show that the optimized BaEVTR VLP enables in vivo editing of β2 microglobulin in long-term human HSPCs (31% at 8 weeks after dosing) and editing of two hemoglobinopathy-relevant loci, BCL11A and HBG1/2 (26% and 7.5%, respectively, at 5 days after dosing), inducing fetal hemoglobin. Our second VLP design uses a CD133-targeted envelope designed to reduce the transduction of mature blood cells and achieves higher in vivo specificity for HSPCs compared to the optimized BaEVTR VLP. As avoiding delivery in filter organs such as the liver would enhance efficiency and safety, we also demonstrate that both VLPs avoid human hepatocytes in a humanized liver model.
Published in
In vivo gene editing of human hematopoietic stem and progenitor cells using envelope-engineered virus-like particles
Botchkarev VV Jr, Harrington S, Stoppato M et al. · Nature biotechnology 2025 · PMID 41350944 · doi:10.1038/s41587-025-02915-2
This dataset
Open GSE267496 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 GSE267496_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 PRJNA1111586. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41350944) ↗
Study design
11 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 11 sample titles: 11 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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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.