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Multicellular liver organoid model for recapitulating hepatitis C virus infection and non-alcoholic fatty liver disease progression [RNA-Seq]

GSE229596 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/05/05 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 6 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)
Hepatitis C virus (HCV) infection has been successfully managed by anti-viral therapies, however, high prevalence to severe chronic liver disease state including non-alcoholic fatty liver disease (NAFLD) is a problem encountered even after the cure of hepatitis C. Moreover, there is currently no reliable in vitro model capable of investigating host-viral interactions and monitoring the progression of viral hepatitis to chronic liver diseases.
Read the full summary ↓
Recent organoid technology has been reported for successful infection of HCV, but there is still lack of non-parenchymal cells, which play a crucial role in disease progression. Here, we provided a novel multicellular liver organoid model using co-culture system of macrophages and liver organoids differentiated from the same cell source, human embryonic stem cells. Surprisingly, HCV infection led potent lipid accumulation in liver organoids through regulation of host lipid metabolism, which was further promoted by macrophage co-culture. Lipid enriched condition provided by longterm-treatment with fatty acid accelerated potent HCV amplification and further promotion of inflammation and fibrosis progression. Therefore, our model may be a valuable novel platform recapitulating diverse phenotypes with host-virus intercommunication and inter-cellular interactions and further progressive features of hepatitis C-associated chronic NAFLD progression of patients with HCV.
This dataset
Open GSE229596 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 GSE229596_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 PRJNA955114 and SRA study SRP432413. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
LO-mono ×2 LO-iMAC ×2 LO-iMAC-FA ×2 LO-mono-HCV ×2 LO-iMAC-HCV ×2 LO-iMAC-FAHCV ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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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