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Comparative transcriptomic points of departure analysis of pesticides in human Caco-2 and Hep G2 cells.

GSE328863 Homo sapiens (human) RNA-seq (gene expression) 960 samples Submitted 2026/07/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 960 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
960 samples: 960 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)
Transcriptomic data capture early molecular perturbations and can be used to derive transcriptomic points of departure (tPODs) as potential alternatives to apical points of departure (PODs) from animal studies. This study aimed to derive tPODs for 19 pesticides of environmental concern using two human cell lines relevant to oral exposure, Caco-2 and Hep G2, and to compare the results with in vivo toxicity data and regulatory thresholds.
Read the full summary ↓
tPODs were successfully obtained for all pesticides, ranging from 0.032 to 880 µM. Pathway analysis indicated more stress-related responses in Caco-2 cells, whereas more metabolic pathways were identified in Hep G2 cells. Following mass balance modeling and in vitro-in vivo extrapolation, tPODs were converted to human administered equivalent doses (t-AEDs). In Hep G2 cells, approximately 70% of t-AEDs were within one order of magnitude of corresponding PODs from chronic animal studies, suggesting moderate concordance. Transcriptomic reference values (t-TRVs) were derived from t-AEDs to compare with regulatory thresholds. Although correlations were weak, the overall trends suggest that short-term in vitro tPODs may provide conservative estimates relative to apical endpoints. Overall, this workflow demonstrates the potential of in vitro transcriptomics to generate quantitative and mechanistic data to support chemical screening and prioritization in risk assessment.
This dataset
Open GSE328863 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 GSE328863_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 PRJNA1456910 and SRA study SRP694174. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
960 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 the first 400 of 960 sample titles: 400 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

+ 920 more — browse all 960 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.