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Effect of JAK inhibitors Upadacitinib or Baricitinib on gene expression during human monocyte-derived macrophage differentiation

GSE232044 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/04/04 Platform GPL23227
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 16 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
Macrophage re-programming by JAK inhibitors relies on MAFBCellular and molecular life sciences : CMLS · 2024
Summary (written by the submitters)
Macrophage phenotypic and functional heterogeneity derives from tissue-specific transcriptional signatures shaped by the local microenvironment. GM-CSF drives the generation of human monocyte-derived macrophages with a potent pro-inflammatory activity upon stimulation. Janus Kinase (JAK) inhibitors are small molecules that reversibly inhibit JAK activity and their subsequent intracellular signaling and have become the treatment of choice for diseases with an inflammatory or immune basis.
Read the full summary ↓
We explored the molecular impact of blocking GM-CSFR-JAK2-STAT5 axis with the Janus Kinase (JAK) inhibitors Upadacitinib or Baricitinib on the gene expression profile in GM-CSF-primed human monocyte derived macrophages.
Published in
Macrophage re-programming by JAK inhibitors relies on MAFB
López-Navarro B, Simón-Fuentes M, Ríos I et al. · Cellular and molecular life sciences : CMLS 2024 · PMID 38528207 · doi:10.1007/s00018-024-05196-1
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 GSE232044_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 PRJNA970703 and SRA study SRP436646. Searching any of these in the dataset finder brings you back here.

Study design
16 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 16 sample titles: 16 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.