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Assessing the Impact of Metabolic Competence on Estrogenic Chemical Bioactivity in High-Throughput Profiling Assays

GSE296915 Homo sapiens (human) RNA-seq (gene expression) Submitted 2025/06/10
At a glance

RNA-seq (gene expression) · human

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)
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.
Paper
Assessing the impact of in vitro xenobiotic metabolism on estrogenic chemical bioactivity in high-throughput profiling assaysToxicology · 2025
Summary (written by the submitters)
High-throughput profiling assays such as high-throughput phenotypic profiling (HTPP) with Cell Painting and high-throughput transcriptomics (HTTr) with TempO-SeqTM are new approach methods that have been used to characterize the bioactivity and potential hazards of chemicals. To better account for potential in vivo hazards in screening, we previously coupled both assays to an in vitro metabolism platform, Alginate Immobilization of Metabolic Enzymes (AIME).
Read the full summary ↓
In this study, we expanded upon our previous results in estrogenic reference chemicals by screening an additional 40 chemicals in VM7Luc4E2 breast cells anticipated to have varied activity and/or shifts in activity with metabolism in an estrogen receptor transactivation assay (ERTA) using the AIME platform. Our results demonstrate how HTTr could detect ER activity and the influence of metabolism on estrogenicity through the activation of ER high-confidence (ERHC) gene signatures and metabolism-induced shifts in ERHC signature enrichment. Additionally, HTPP could detect cases where metabolism impacted chemical cytotoxicity and detect generation of metabolites that were bioactive in a different way than their respective parent compounds when comparing the profiles of active features between AIME conditions. Notably, our findings highlighted examples of chemicals that had very different phenotypic and gene expression profiles between metabolic conditions that would not be observed in traditional chemical screening in most cell models. This study exhibited how high-throughput profiling assays could distinguish changes in bioactivity with metabolic competence using the AIME platform, which will ultimately better inform next generation risk assessment by providing more comprehensive hazard characterizations.
Published in
Assessing the impact of in vitro xenobiotic metabolism on estrogenic chemical bioactivity in high-throughput profiling assays
Jurgelewicz A, Breaux K, Willis CM et al. · Toxicology 2025 · PMID 40543886 · doi:10.1016/j.tox.2025.154215
This dataset
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.

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 GSE296915_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1257832 and SRA study SRP582627. Searching any of these in the dataset finder brings you back here.

Samples in this study

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