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Generation and characterization of mature hepatocyte organoids for liver metabolic studies

GSE245632 Mus musculus (mouse) RNA-seq (gene expression) 45 samples Submitted 2024/05/15 Platform GPL21626Platform GPL32159
At a glance

RNA-seq (gene expression) · mouse · 45 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
45 samples: 15 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
Generation and characterization of mature hepatocyte organoids for liver metabolic studiesJournal of cell science · 2024
Summary (written by the submitters)
The liver plays a central role in whole body metabolic regulation. Studies of liver metabolism are mostly done in vivo in animal models due to the lack of reliable in vitro systems that can recapitulate liver metabolic functions and their regulation. Hepatocyte organoids (HOs) generated in vitro recently are powerful tools for liver regeneration.
Read the full summary ↓
Here we developed a novel culture condition that enabled successful generation of mature hepatocyte organoids (MHOs) with metabolic functions characteristic of adult livers. We showed that the MHOs can be used to study gene expression that exhibit zonal patterns in vivo, hepatic proliferation during tumorigenesis and metabolic alterations in disorders of both alcoholic and non-alcoholic fatty liver diseases.
Published in
Generation and characterization of mature hepatocyte organoids for liver metabolic studies
Liu Y, Zhou Y, Ahodantin J et al. · Journal of cell science 2024 · PMID 38700490 · doi:10.1242/jcs.261961
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 GSE245632_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 PRJNA1029192 and SRA study SRP466902. Searching any of these in the dataset finder brings you back here.

Study design
15 conditions, mostly in triplicate
Hepatocyte organoids, +GF, ×3 Hepatocyte organoids, RGF, Day 3, ×3 Hepatocyte organoids, RGF +T3, Day 3, ×3 Hepatocyte organoids, RGF +T3 +DEX, Day 3, ×3 Hepatocyte organoids, RGF, Day 6, ×3 Hepatocyte organoids, RGF +T3, Day 6, ×3 Hepatocyte organoids, RGF +T3 +DEX, Day 6, ×3 Hepatocyte organoids, RGF +DEX, Day 6, ×3 +7 more

Supports a between-group comparison across 45 samples.

How we read this: 15 replicated groups read from 45 sample titles; they account for 45 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

+ 5 more — browse all 45 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.