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Brd4 knockout colonic epithelial organoids are partially resistant to IFNg-induced cell death.

GSE248821 Mus musculus (mouse) RNA-seq (gene expression) 13 samples Submitted 2024/11/24 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 13 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
13 samples: 4 × control, non-target gRNA vs 3 × Brd4 KO, vs 3 × control, IFNg, vs 3 × Brd4 KO, IFNg,Groups 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
Inhibition of BRD4 attenuated IFNγ-induced apoptosis in colorectal cancer organoidsBMC cancer · 2025
Summary (written by the submitters)
To identify the role of Brd4 in colorectal cancer (CRC) development , we performed Sleeping Beauty (SB) transposon mutagenesis in mice. We identified several candidate genes including Brd4. To provide functional validation, we knocked out Brd4 in mouse tumor organoids carrying an activating KrasG12D, and an inactivating ApcD716 allele, and performed RNAseq.
Read the full summary ↓
Brd4 knockout colonic organoids showed deregulation of genes in IFNg signaling. We treated Brd4 knockout organoids with IFNg and found that Brd4 konckout organoids showed partial resistance to IFNg-induce cell death. The mechanism may explain the function of Brd4 in colon cancer development.
Published in
Inhibition of BRD4 attenuated IFNγ-induced apoptosis in colorectal cancer organoids
Yonezawa A, Shimomura K, Okamoto K et al. · BMC cancer 2025 · PMID 39849410 · doi:10.1186/s12885-025-13544-y
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 GSE248821_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 PRJNA1046066 and SRA study SRP474740. Searching any of these in the dataset finder brings you back here.

Study design
4 × control, non-target gRNA vs 3 × Brd4 KO, vs 3 × control, IFNg, vs 3 × Brd4 KO, IFNg,

Supports a between-group comparison across 13 samples.

How we read this: 4 replicated groups read from 13 sample titles; they account for 13 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
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