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On target: Transcriptomic analysis of 12 human iPSC-derived cell phenotypes shows precision in an extensive pharmacological investigation [set 2]

GSE295447 Homo sapiens (human) RNA-seq (gene expression) 840 samples Submitted 2025/04/30 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 840 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
840 samples: 840 samples, each with a distinct title — no replicated groupsGroups 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.
Summary (written by the submitters)
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.
Read the full summary ↓
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.
This dataset
Open GSE295447 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 GSE295447_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 PRJNA1254692 and SRA study SRP580876. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
840 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from the first 400 of 840 sample titles: 400 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

+ 800 more — browse all 840 samples with per-sample file links →

Datasets on a similar topic

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.