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Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

GSE262419 Homo sapiens (human) RNA-seq (gene expression) 4558 samples Submitted 2024/07/23 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 4558 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
4558 samples: 22 × Plate1_D vs 12 × Plate1_C vs 6 × Plate1_EGroups 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
Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived CardiomyocytesChemical research in toxicology · 2024
Summary (written by the submitters)
Cardiovascular disease remains a significant global health concern, yet the cardiotoxic effects of environmental chemicals are largely unexplored. Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-Cardiomyocytes) are well-established as valuable high-throughput cardiotoxicity testing model based on the analysis of various beating parameters; however, additional adverse effects that may not be directly related to ion channel activity are difficult to ascertain from traditional assays.
Read the full summary ↓
Transcriptomic data may provide a more comprehensive assessment for additional molecular responses and serve as a basis for dose-response analysis. We hypothesized that transcriptomic data and functional phenotypic measurements in iPSC-Cardiomyocytes could be used to effectively screen for both hazard and risk associated with chemical exposures. To test this hypothesis, we performed concentration-response analysis of 464 chemicals from 12 diverse chemical classes. Functional effects (beat frequency, QT-prolongation, and asystole) and cytotoxicity were measured. In addition, whole transcriptome RNA-seq was used to investigate global gene expression profiles. Points of departure (PODs) were derived from both phenotypic endpoints and transcriptomic data. Margin of safety (MOS) for drugs and margin of exposure (MOE) for non-drug chemicals were calculated using PODs and the exposure estimates. The phenotypic assays revealed that a varying proportion (10% to 44%) of chemicals in each class had effects on iPSC-Cardiomyocytes; positive chronotrope was the most sensitive phenotype. Overall, 244 (53%) chemicals were active in at least one functional phenotype. As expected, drugs with known adverse effects on the heart were most active. Transcriptomic data showed 69 (15%) had significant effects on the biological pathways, the pathways that were significantly affected by some tested compounds were highly relevant to cardiomyocyte function. Specifically, the enriched pathways aligned well with key characteristics of known cardiovascular toxicants, with mitochondrial dysfunction being the most affected key characteristic. Using exposure and hazard (transcriptomic and phenotypic PODs from this study), we derived MOS and MOE and found that drugs tend to have lower MOS than MOE for the environmental chemicals. Thirteen and 10 drugs had MOS < 1 using phenotypic and transcriptomic data, respectively, suggesting that further investigation may be needed for these substances to test for potential cardiovascular risk. Overall, our findings demonstrate how the integrative use of in vitro transcriptomic data and phenotypic assays in iPSC-Cardiomyocytes not only offers a unique and complementary approach for hazard and risk prioritization, but also offers comprehensive mechanistic support for in vitro test results.
Published in
Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes
Tsai HD, Ford LC, Burnett SD et al. · Chemical research in toxicology 2024 · PMID 39046974 · doi:10.1021/acs.chemrestox.4c00193
This dataset
Open GSE262419 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 GSE262419_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 PRJNA1091686 and SRA study SRP497764. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39046974) ↗
Study design
22 × Plate1_D vs 12 × Plate1_C vs 6 × Plate1_E

Supports a between-group comparison across 40 samples.

How we read this: 3 replicated groups read from the first 40 of 4558 sample titles; they account for 40 of them. 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

+ 4518 more — browse all 4558 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.