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Applications for estimation of in vivo toxicity point of departure for discovery stage molecule predictive safety assessment

GSE269501 Rattus norvegicus (rat) RNA-seq (gene expression) 570 samples Submitted 2025/06/28 Platform GPL25947Platform GPL24676
At a glance

RNA-seq (gene expression) · rat · 570 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
Rattus norvegicus (rat)
Samples
570 samples: 364 distinct titles, 20 of them repeatedGroups 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)
Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics-based methods could provide powerful tools for risk assessment and regulatory decision-making. However, using these models generates challenges translating in vitro concentrations to corresponding in vivo internal exposures. We tested whether in vivo (rat liver) transcriptional and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptional PODs or HepaRG Cell Painting PODs using PBPK modeling.
Read the full summary ↓
We compared two PBPK models, ADMET predictor and the httk R package, and found httk to predict the rat hepatocyte no observed transcriptional effect level (NOTEL)-derived PODs more accurately. Our findings suggest that a rat liver apical and transcriptome POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE.
This dataset
Open GSE269501 in finder → Find more 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 GSE269501_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 PRJNA1122150 and SRA study SRP513045. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
364 distinct titles, 20 of them repeated
3MC_d0.3_Rep01_P ×3 PHB_50_Rep01_P ×3 3MC_d0.3_Rep02_P ×3 PHB_50_Rep02_P ×3 DMSO_Rep01_P ×3 PHB_50_Rep03_P ×3 3MC_d0.3_Rep03_P ×3 DMSO_Rep07_P ×3 +12 more

How we read this: Read from the first 400 of 570 sample titles: 364 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

+ 530 more — browse all 570 samples with per-sample file links →

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