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Integral approach to organelle profiling in human iPSC-derived cardiomyocytes enhances in-vitro cardiac-safety classification

GSE289264 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2025/08/15 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 54 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
54 samples: 18 conditions, mostly in triplicateGroups 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
Integral approach to organelle profiling in human iPSC-derived cardiomyocytes enhances in vitro cardiac safety classification of known cardiotoxic compoundsFrontiers in toxicology · 2025
Summary (written by the submitters)
Efficient preclinical prediction of cardiovascular side effects poses a pivotal challenge for the pharmaceutical industry. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are becoming increasingly important in this field due to inaccessibility of human native cardiac tissue. Functional changes in hiPSC-CMs such as electrophysiological abnormalities, are commonly studied and considered clinically predictive.
Read the full summary ↓
However, other clinically-relevant safety parameters, such as structural toxicity, are less understood. This study utilized hiPSC-CMs from multiple donors, cultured in serum-free conditions and treated with a library of 17 small molecules with stratified cardiac side effects, to establish an in-vitro drug screening protocol that leverages the power of morphological profiling with established functional readouts. High-content imaging of subcellular organelles, combined with multi-electrode array (MEA) data, was analyzed to identify cardiac safety clusters. Both supervised and unsupervised clustering revealed patterns associated with known clinical side effects. Overall, clustering based on morphological data more accurately reflected clinical classifications than electrophysiology alone. RNA-sequencing was used to support the mechanistic insights derived from morphological profiling, validating the former as a valuable cardiotoxicity tool. Results demonstrate that a combined approach of analyzing morphology, and electrophysiology enhances in-vitro prediction and understanding of drug cardiotoxicity.
Published in
Integral approach to organelle profiling in human iPSC-derived cardiomyocytes enhances in vitro cardiac safety classification of known cardiotoxic compounds
Szabo BR, Stein J, Savchenko A et al. · Frontiers in toxicology 2025 · PMID 40918936 · doi:10.3389/ftox.2025.1644119
This dataset
Open GSE289264 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 GSE289264_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 PRJNA1222304 and SRA study SRP563155. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40918936) ↗
Study design
18 conditions, mostly in triplicate
hiPSC-CMs treated with doxorubicin biol ×3 hiPSC-CMs treated with cisplatin biol ×3 hiPSC-CMs treated with ponatinib biol ×3 hiPSC-CMs treated with dasatinib biol ×3 hiPSC-CMs treated with lapatinib biol ×3 hiPSC-CMs treated with 5-fluoro-uracil biol ×3 hiPSC-CMs treated with methotrexate biol ×3 hiPSC-CMs treated with omecamtiv_mecarbil biol ×3 +10 more

Supports a between-group comparison across 54 samples.

How we read this: 18 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 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.