GEO series

Comparative analysis of gene expression in HDV-infected dHepaRG cells upon treatment with IFN-α-2a or IFN-λ1

GSE324785 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2026/03/13 Platform GPL34284

At a glance

RNA-seq (gene expression) · human · 27 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: 27 samples: 9 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.

Summary (written by the submitters)

Hepatitis D virus (HDV) causes the most severe form of chronic viral hepatitis, often leading to advanced liver disease and hepatocellular carcinoma. Viral cure is rarely achieved in infected patients. Interferon-alpha (IFN-α)-based therapies show suboptimal efficacy and low rates of sustained response, as reflected by their limited effect on HDV replication in vitro.

Read the full summary ↓

Here, we show that HDV infection induces cellular resistance to IFN-α, marked by impaired STAT1 phosphorylation and reduced expression of interferon-stimulated genes (ISGs) in hepatocyte-like cells. This refractoriness depends on virus-induced innate immune activation, highlighting the role of HDV-induced ISG expression in regulating IFN signalling. We identify USP18 as a key mediator of IFN-α resistance in infected cells. Notably, ISG expression in response to type III IFN (IFN-λ) remains intact, consistent with USP18’s selective inhibition of IFN-α signalling. Collectively, these findings reveal the molecular mechanism of IFN-α resistance in HDV-infected hepatocytes and provide a rationale for developing novel therapies against this major public health threat.

This dataset

Open GSE324785 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 GSE324785_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 PRJNA1437063 and SRA study SRP683443. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

9 conditions, mostly in triplicate

dHepaRG cells, mock, ×3 dHepaRG cells, IFNa, ×3 dHepaRG cells, IL29, ×3 dHepaRG cells, SeV, ×3 dHepaRG cells, ECMV, ×3 dHepaRG cells, mockPEG, ×3 dHepaRG cells, HDV, ×3 dHepaRG cells, HDVIFNa, ×3 +1 more

Supports a between-group comparison across 27 samples.

How we read this: 9 replicated groups read from 27 sample titles; they account for 27 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

GSM9585841 dHepaRG cells, mock, rep1 dHepaRG cells, mock,
GSM9585842 dHepaRG cells, IFNa, rep1 dHepaRG cells, IFNa,
GSM9585843 dHepaRG cells, IL29, rep1 dHepaRG cells, IL29,
GSM9585844 dHepaRG cells, SeV, rep1 dHepaRG cells, SeV,
GSM9585845 dHepaRG cells, ECMV, rep1 dHepaRG cells, ECMV,
GSM9585846 dHepaRG cells, mockPEG, rep1 dHepaRG cells, mockPEG,
GSM9585847 dHepaRG cells, HDV, rep1 dHepaRG cells, HDV,
GSM9585848 dHepaRG cells, HDVIFNa, rep1 dHepaRG cells, HDVIFNa,
GSM9585849 dHepaRG cells, HDVIL29, rep1 dHepaRG cells, HDVIL29,
GSM9585850 dHepaRG cells, mock, rep2 dHepaRG cells, mock,
GSM9585851 dHepaRG cells, IFNa, rep2 dHepaRG cells, IFNa,
GSM9585852 dHepaRG cells, IL29, rep2 dHepaRG cells, IL29,
GSM9585853 dHepaRG cells, SeV, rep2 dHepaRG cells, SeV,
GSM9585854 dHepaRG cells, ECMV, rep2 dHepaRG cells, ECMV,
GSM9585855 dHepaRG cells, mockPEG, rep2 dHepaRG cells, mockPEG,
GSM9585856 dHepaRG cells, HDV, rep2 dHepaRG cells, HDV,
GSM9585857 dHepaRG cells, HDVIFNa, rep2 dHepaRG cells, HDVIFNa,
GSM9585858 dHepaRG cells, HDVIL29, rep2 dHepaRG cells, HDVIL29,
GSM9585859 dHepaRG cells, mock, rep3 dHepaRG cells, mock,
GSM9585860 dHepaRG cells, IFNa, rep3 dHepaRG cells, IFNa,
GSM9585861 dHepaRG cells, IL29, rep3 dHepaRG cells, IL29,
GSM9585862 dHepaRG cells, SeV, rep3 dHepaRG cells, SeV,
GSM9585863 dHepaRG cells, ECMV, rep3 dHepaRG cells, ECMV,
GSM9585864 dHepaRG cells, mockPEG, rep3 dHepaRG cells, mockPEG,
GSM9585865 dHepaRG cells, HDV, rep3 dHepaRG cells, HDV,
GSM9585866 dHepaRG cells, HDVIFNa, rep3 dHepaRG cells, HDVIFNa,
GSM9585867 dHepaRG cells, HDVIL29, rep3 dHepaRG cells, HDVIL29,

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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.