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Effect of siRNA-mediated GSK3 knock-down on the transcriptional signature of human GM-CSF-dependent monocyte-derived macrophages

GSE266236 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/04/16 Platform GPL23227
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 12 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
Reprogramming of GM-CSF-dependent alveolar macrophages through GSK3 activity modulationeLife · 2025
Summary (written by the submitters)
The functional versatility of macrophages is intrincately tied to factors such as their ontogeny and the specific tissue and extracellular environment. Monocyte-derived macrophages are oppositely instructed by M-CSF or GM-CSF. GM-CSF drives monocyte-derived macrophages towards heightened pro-inflammatory activity and the acquisition of the lung alveolar macrophage phenotype and gene profile whereas M-CSF gives rise to anti-inflammatory, pro-resolving, and immunosuppressive monocyte-derived macrophages.
Read the full summary ↓
We explored the molecular impact of siRNA mediated knocking-down GSK3A, GSK3B or both (GSK3A/B) on the gene expression profile of GM-CSF-primed human monocyte derived macrophages. GSK3A/B knowdown skewed the transcriptional profile of GM-MØ towards an anti-inflammatory phenotype.
Published in
Reprogramming of GM-CSF-dependent alveolar macrophages through GSK3 activity modulation
Ríos I, Herrero C, Torres-Torresano M et al. · eLife 2025 · PMID 40365863 · doi:10.7554/eLife.102659
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 GSE266236_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: 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 PRJNA1106426 and SRA study SRP504892. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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
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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.