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High-throughput transcriptomics screening of mixtures of BPA alternatives reveal additive effects in vitro

GSE276869 Homo sapiens (human) RNA-seq (gene expression) 672 samples Submitted 2025/06/30 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 672 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
672 samples: 672 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.
Paper
High-throughput transcriptomics analysis of equipotent and human relevant mixtures of BPA alternatives reveal additive effects in vitroArchives of toxicology · 2025
Summary (written by the submitters)
Humans are regularly exposed to mixtures of BPA alternative chemicals through their diets, consumer products and other sources. Like BPA, many alternatives perturb nuclear hormone receptors and are considered endocrine disrupting chemicals. We used high-throughput transcriptomics (HTTr) to evaluate the potency and potential modes of action of seven mixtures of BPA alternatives, along with their 12 individual components, in MCF-7 breast cancer cells.
Read the full summary ↓
Our primary aim was to explore whether alternatives present in mixtures act additively. MCF-7 cells were exposed to chemicals (0.001 – 50 µM) for 48 h in serum-free conditions and TempO-Seq (BioSypder Inc.) was used to determine global and estrogen receptor alpha (ERα)-specific transcriptomic changes. Transcriptomic points of departure were derived using benchmark concentration (BMC) modelling. We first identified the concentration at which global transcriptional activity was robustly altered. Then, a novel ERα transcriptomic biomarker was used to identify ERα agonists and model a predicted ERα activation point of departure. Mixtures modelling was employed to predict potency of BPA alternatives and test for additive effects in vitro. Ingenuity Pathway Analysis (IPA; Qiagen) was used to identify upstream regulators and canonical pathways from genes fitting BMCs. All seven mixtures had additive effects across all transcriptomic points of departure modelled. BPAF was the most potent individual chemical tested overall, followed by BPA and BPC. The ERα transcriptomic biomarker classified all mixtures as ERα activators along with several individual BPA alternatives. Pathway analysis revealed that all mixtures and most individual components perturbed similar upstream regulators and pathways, suggesting common modes of action. These data support the use of HTTr as part of new approach methodologies to assess the toxicological potency of mixtures in vitro.
Published in
High-throughput transcriptomics analysis of equipotent and human relevant mixtures of BPA alternatives reveal additive effects in vitro
Matteo G, Cho E, Rigden M et al. · Archives of toxicology 2025 · PMID 40531193 · doi:10.1007/s00204-025-04110-3
This dataset
Open GSE276869 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 GSE276869_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 PRJNA1159318 and SRA study SRP531942. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40531193) ↗
Study design
672 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 672 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

+ 632 more — browse all 672 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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