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Single Cell Transcriptional Perturbome in Pluripotent Stem Cell Models

GSE267655 Homo sapiens (human) Single-cell RNA-seq 384 samples Submitted 2025/09/30 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 384 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
384 samples: 384 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)
Functional genomics screens in human induced pluripotent stem cell (hiPSC) models offer immense potential yet remain challenging. We developed iPS2-seq: iPS-optimized inducible Postranscriptional Silencing in pool deconvoluted by single cell sequencing.
Read the full summary ↓
This method allows phenotype-agnostic screens in hPSCs and their derivatives through loss-of-function perturbations that are mRNA-depleting (bypassing genotoxicity), clonally traced (controlling genetic and epigenetic variability), single cell aware (accounting for asyncronous and heterogeneous differentiation), isogenic engineered (preventing silencing), inducible and reversible (enabling stage-specific studies and robust control-treatment paired analyses). iPS2-seq is compatible with both commercial microfluidics and homebrew split-pool scRNA-seq protocols, enabling screens with different scales, costs, and input materials. A dedicated bioinformatics pipeline, catcheR, supports iPS2-seq design and analysis. We demonstrate this by studying congenital heart disease-associated genes in hiPSC-derived monolayer cardiomyocytes and cardiac organoids, identifying a key role for SMAD2 in cardiomyogenesis. iPS2-seq strengthens and democratizes access to functional single cell genomics in and beyond hiPSC-derived organoids.
This dataset
Open GSE267655 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 GSE267655_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1112344 and SRA study SRP508138. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
384 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 384 sample titles: 384 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

+ 344 more — browse all 384 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.