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Stable lentiviral-mediated expression of Cytochrome P450 2D6 in HepaRG cells: New means for in vitro assessment of xenobiotic biotransformation and cytotoxicity.

GSE297830 Homo sapiens (human) RNA-seq (gene expression) 90 samples Submitted 2025/09/30 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 90 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
90 samples: 90 samples, each with a distinct title — no replicated groupsGroups 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)
The human hepatic HepaRG cell line is often considered as the closest surrogate to primary culture of normal human hepatocytes (PHH) for toxicity studies. However, differentiated HepaRGcells express very low levels of the cytochrome P450 2D6 (CYP2D6) protein, which is essential for the biotransformation of nearly 25% of drugs on the market.
Read the full summary ↓
To overcome this limitation, infection of progenitor HepaRG cells were performed using lentiviral particles containing a transgene encoding a single mRNA translated into a polypeptide undergoing proteolytic cleavage via the T2A peptide to produce both human CYP2D6 and GFP proteins. Differentiated HepaRG cells transduced with this lentivirus stably expressed GFP and catalytically active CYP2D6 enzyme at levels close to those found in PHH with high CYP2D6 activities. Using the CYP2D6 transgenic HepaRG cells, we showed that tramadol was metabolized into both, N- and O-desmethyl tramadol as observed in human serum in contrast with the production of N-desmethyl tramadol only in parental HepaRG cells via the CYP3A4 activity. Similarly, after perhexiline (PHX) treatments, higher IC50 were found in CYP2D6 expressing HepaRG cells associated to lower mitochondrial damages compared to those found in parental cells for the same PHX concentrations. Gene profiling between parental and transgenic cells demonstrated that the CYP2D6 expressing HepaRG cells had kept their ability to proliferate and differentiate with low impact on the expression of the hepatocyte specific functions, however, we identified a limited set of genes including NXF3 and TRIM63 that were up-regulated in CYP2D6 expressing HepaRG cells. By knocking-down GFP and CYP2D6 expression using CRISP/Cas9 technology in the transgenic cell line, we demonstrated that NXF3 and TRIM63 induction was triggered by the mRNA encoding GFP and CYP2D6 but not by the lentiviral transgene inserted into the HepaRG cell genome. Together, these data confirmed that the CYP2D6 transgenic HepaRG cells represent a suitable optimized transgenic model of HepaRG cells to evaluate biotransformation and toxicity of specific compounds metabolized by CYP2D6.
This dataset
Open GSE297830 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 GSE297830_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 PRJNA1266246 and SRA study SRP586890. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
90 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 90 sample titles: 90 distinct titles with little repetition. 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

+ 50 more — browse all 90 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.