← BioTransfer GEO Dataset Finder
GEO series

On target: Transcriptomic analysis of 12 human iPSC-derived cell phenotypes shows precision in an extensive pharmacological investigation [set 1]

GSE295444 Homo sapiens Expression profiling by high throughput sequencing 840 samples 2025/04/30 GPL20301
Summary
The specificity of action of a pharmaceutical compound to a particular target cell type is desirable to mitigate undesired effects in off target cell types. However, drug screening strategies necessarily employ cell lines that aim to represent but are potentially poor surrogates for the target cell phenotype. In this study, we addressed whether transcriptomic similarity between cell phenotpyes is predictive of their drug response. Thus, we differentiated hiPSC from three individuals into twelve different distinct phenotypes (cardiomyocytes, smooth muscle cells, hepatocytes, renal proximal tubular cells, podocytes, cortical neurons, dopaminergic neurons, neural progenitor cells, nociceptors, astrocytes, macrophages and endothelial cells). We exposed these cells and the MCF-7 cancer cell line with 12 compounds (Taxol (Paclitaxel) (0.04 µM), 17-AAG (Tanespimycin) (0.2 µM), Bardoxolone methyl (CDDO-Me) (0.2 µM), Rapamycin (Sirolimus) (0.2 µM), Tunicamycin (0.2 µM), Vorinostat (0.2 µM), A-366 (1 µM), A-971432 (1 µM), Compound 81 (1 µM), Rosiglitazone (1 µM), Thiamet-G (1 µM), Lithium chloride (5000 µM)) for 7 hours. Analysis of the RNAseq profiles revealed that the impact of drug treatment varies significantly among different cell types. Despite some compounds showing consistent effects across various cell types, there was no clear correlation between transcriptomic similarity and the magnitude of response to the same drug. These findings underscore that accurate predictions of drug responses necessitate employing the most representative models of the target cell types involved.
Download
NCBI GEO page ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
Similar datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.