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9-cis-retinoic acid improves disease modelling in iPSC-derived liver organoids

GSE292959 Homo sapiens (human) RNA-seq (gene expression) 33 samples Submitted 2025/06/26 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 33 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
33 samples: 8 conditions, mostly in duplicateGroups 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)
Liver fibrosis majorly impacts global health, necessitating development of in vitro models to study disease mechanisms and develop drug therapies. Relevant models should at least include hepatocytes and hepatic stellate cells (HSCs) and ideally use three-dimensional cultures to mimic in vivo conditions. Induced pluripotent stem cells (iPSCs) allow patient-specific liver modelling, but current models based on iPSC-derived hepatocytes (iHepatocytes) and HSCs (iHSCs) still lack key functions.
Read the full summary ↓
We developed organoids of iHepatocytes and iHSCs, and compared them to HepaRG and primary HSC organoids. RNA-sequencing analysis comparison of these cultures identified a potential role for the transcription factor RXRA in hepatocyte differentiation and HSC quiescence. Treating cells with the RXRA ligand 9-cis-retinoic acid (9CRA) promoted iHepatocyte metabolism and iHSC quiescence. In organoids, 9CRA enhanced fibrotic response to TGF-β and acetaminophen, highlighting its potential for refining iPSC-based liver fibrosis models to more faithfully replicate human drug-induced liver injury and fibrotic conditions.
This dataset
Open GSE292959 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 GSE292959_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 PRJNA1242238 and SRA study SRP573330. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in duplicate
HEP_iHSC ×4 HepaRG ×3 iHSC1_D ×2 iHSC2_D ×2 iHSC3_D ×2 iHSC4_D ×2 iHSC5_D ×2 iHSC6_D ×2 +14 more

Supports a between-group comparison across 19 samples.

How we read this: 8 replicated groups read from 33 sample titles; they account for 19 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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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.