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IGF2BP3 regulates RNA metabolism during unfolded protein response [RKO iCas9 cells]

GSE289480 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2026/07/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 40 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
40 samples: 10 conditions, mostly with about 4 replicates eachGroups 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.
Summary (written by the submitters)
Accumulation of misfolded proteins in the endoplasmic reticulum (ER) perturbs cellular homeostasis and leads to pathological conditions termed ER stress. The Unfolded Protein Response (UPR) is a highly conserved signaling cascade that maintains ER homeostasis. While the transcriptional response is paramount for UPR signaling, several mRNAs are regulated at the posttranscriptional level, yet how this is achieved remains poorly understood.
Read the full summary ↓
Here, we show that a highly conserved RNA-binding protein, IGF2BP3, interacts with transcripts encoding for the UPR target genes and regulates the UPR signaling. During ER stress, IGF2BP3 destabilized most of its targets, including the UPR target RNAs. On the other hand, IGF2BP3 stabilized a subset of mRNAs encoding for transcription regulators through this indirectly regulated transcription of the UPR target genes. This feedback loop allows IGF2BP3 to elevate the levels of stress response genes while leading to the degradation of other transcripts during ER stress. Our results reveal a novel IGF2BP3-driven gene regulatory network contributing to cellular adaptation to ER stress.
This dataset
Open GSE289480 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 GSE289480_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 PRJNA1223285 and SRA study SRP563687. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly with about 4 replicates each
sgAAVS1, DMSO control, ×4 sgAAVS1, Dox, ×4 sgIGF2BP2, DMSO control, ×4 sgIGF2BP2, Dox, ×4 sgIGF2BP2, siControl, ×4 sgIGF2BP2, siIGF2BP2, ×4 sgIGF2BP3, DMSO control, ×4 sgIGF2BP3, Dox, ×4 +2 more

Supports a between-group comparison across 40 samples.

How we read this: 10 replicated groups read from 40 sample titles; they account for 40 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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