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Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts [Chromatin associated RNA-seq]

GSE218968 Mus musculus (mouse) RNA-seq (gene expression) 27 samples Submitted 2024/04/16 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 27 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
Mus musculus (mouse)
Samples
27 samples: 9 conditions, mostly in triplicateGroups 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
An integrative epigenome-based strategy for unbiased functional profiling of clinical kinase inhibitorsMolecular systems biology · 2024
Summary (written by the submitters)
More than 500 kinases are implicated in the control of virtually every cellular process in mammals and deregulation of their activity is causally linked to diseases ranging from cancer to inflammatory and degenerative disorders. In the last three decades, massive worldwide efforts led to the approval of 62 clinical kinase inhibitors (CKI), with hundreds of additional molecules in various stages of pre-clinical or clinical development.
Read the full summary ↓
In addition to the intended target(s), small molecule CKI commonly inhibit multiple additional kinases, resulting in both enhanced clinical effects and undesired side effects that are largely unpredictable based on in vitro selectivity profiling. To complement current CKI profiling methods, we set out a novel experimental and analytical approach grounded on the use of chromatin modifications as unbiased and information-rich readouts of the functional effects of CKI on macrophage activation, a complex biological response of biomedical relevance. This approach allowed us to characterize and dissect the distinctive behaviors of CKI with identical intended targets as well as to discover undescribed activities of CKI on macrophage activation programs.
Published in
An integrative epigenome-based strategy for unbiased functional profiling of clinical kinase inhibitors
Gualdrini F, Rizzieri S, Polletti S et al. · Molecular systems biology 2024 · PMID 38724853 · doi:10.1038/s44320-024-00040-x
This dataset
Open GSE218968 in finder → Find more mouse 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 GSE218968_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 PRJNA906475 and SRA study SRP410397. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38724853) ↗
Study design
9 conditions, mostly in triplicate
IL4_1h ×3 IL4_2h ×3 IL4_30 ×3 IL4_4h ×3 LPS_1h ×3 LPS_2h ×3 LPS_30 ×3 LPS_4h ×3 +1 more

Supports a between-group comparison across 27 samples.

How we read this: 9 replicated groups read from 27 sample titles; they account for 27 of them. 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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