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Incorporating Metabolic Competence into High-Throughput Profiling Assays

GSE292777 Homo sapiens (human) RNA-seq (gene expression) 192 samples Submitted 2025/04/29 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 192 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
192 samples: 182 distinct titles, 10 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.
Paper
Incorporating Metabolic Competence into High-Throughput Profiling AssaysToxicological sciences : an official journal of the Society of Toxicology · 2025
Summary (written by the submitters)
One limitation of previous high-throughput transcriptomics (HTTr) chemical screening studies using TempO-Seq is that most of the in vitro models used on those studies lack instrinsic capabilites to metabolize xenobiotics. This study incorporated an in vitro metabolism platform, aliginate immobilization of metabolic enzymes (AIME), into the dosing procedures for HTTr screening to address this limitation.
Read the full summary ↓
The 384-well, lid-based platform is incubated for 2 hours on standard microplates containing chemicals, metabolic co-factors and cell culture medium to make "conditioned" medium. This medium is used to treat cells rather than treated them with parent chemicals directly, and the AIME platform does not impact downstream procedures for preparation of cell lysates that are compatible with the TempO-Seq assay. The AIME platform was used concurrently in negative or "nomet" (water + alginate) and positive or "met" (hepatic S9 + alginate) metabolic conditions. This proof-of-concept experiment screened three previously studied estrogenic reference chemicals representing three "shifts" with metabolism in the VM7Luc4E2 breast cell line in an estrogen receptor transactivation assay: bioinactivation, bioactivation or no change with metabolism. Our results indicated that the TempO-Seq assay could detect changes in bioactivity with metabolism. The established workflow can be used in future studies to improve human relevance in our chemical screening efforts.
Published in
Incorporating Metabolic Competence into High-Throughput Profiling Assays
Jurgelewicz A, Breaux K, Willis CM et al. · Toxicological sciences : an official journal of the Society of Toxicology 2025 · PMID 40317128 · doi:10.1093/toxsci/kfaf061
This dataset
Open GSE292777 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 GSE292777_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 PRJNA1241242 and SRA study SRP572671. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40317128) ↗
Study design
182 distinct titles, 10 of them repeated
NoMet_DMSO_0_0uM_TC00003588_B ×2 NoMet_DMSO_0_0uM_TC00003588_F ×2 Met_DMSO_0_0uM_TC00003591_B ×2 Met_DMSO_0_0uM_TC00003591_G ×2 NoMet_DMSO_0_0uM_TC00003594_B ×2 NoMet_DMSO_0_0uM_TC00003594_F ×2 Met_DMSO_0_0uM_TC00003597_B ×2 Met_DMSO_0_0uM_TC00003597_G ×2 +2 more

How we read this: Read from 192 sample titles: 182 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

+ 152 more — browse all 192 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.