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Related Experiment Video

Updated: Jun 16, 2025

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells.

Joseph L Bundy1, Logan J Everett1, Jesse D Rogers2

  • 1Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.

Toxicology and Applied Pharmacology
|August 19, 2024
PubMed
Summary

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Decoding cellular stress states for toxicology using single-cell transcriptomics.

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Can graph similarity metrics be helpful for analogue identification as part of a read-across approach?

Computational toxicology (Amsterdam, Netherlands)·2026

New approach methodologies, like High Throughput Transcriptomics (HTTr), accelerate chemical risk assessment using cell-based assays. This study demonstrates HTTr’s reproducibility and utility in predicting chemical potency and mechanisms of action.

Area of Science:

  • Toxicology
  • Genomics
  • Computational Biology

Background:

  • New Approach Methodologies (NAMs) aim to improve chemical risk assessment by reducing animal testing and costs.
  • High Throughput Transcriptomics (HTTr) is an emerging NAM for hazard evaluation, offering in vitro points-of-departure and mechanistic insights.

Purpose of the Study:

  • To screen 1201 chemicals using HTTr to evaluate bioactivity and identify biological pathway altering concentrations (BPACs).
  • To assess the reproducibility and predictive capacity of HTTr in chemical hazard assessment.

Main Methods:

  • Screening of 1201 chemicals using HTTr in U-2 OS cells over 24 hours at eight concentrations.
  • Analysis of transcriptomics data using gene set enrichment analysis to create signature scores, followed by concentration-response modeling.
Keywords:
Computational toxicologyHigh Throughput TranscriptomicsSignature scoringU-2 OS

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  • Assessment of assay reproducibility using reference chemicals and comparison of BPACs with Cell Painting data.

    • Main Results:

    • High reproducibility of BPACs across replicates for reference chemicals (SD as low as 5.6 × 10⁻³ μM).
    • Modest agreement (R² = 0.55) between HTTr-derived BPACs and Cell Painting phenotype altering concentrations.
    • Successful prediction of putative mechanisms of action for screened chemicals.

    Conclusions:

    • HTTr is a reproducible and valuable tool for chemical hazard evaluation, providing potency estimates and mechanistic insights.
    • The study contributes to a growing public repository of transcriptomic data for chemical risk assessment.
    • Cell-based molecular profiling methods like HTTr are effective for assessing chemical potency and predicting mechanisms across diverse chemicals.