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Temporal Uncoupling of Radial Glial Progenitor Cell Lineage Progression in Self-organizing Organoid System – MADM-CloneSeq

GSE327468 Mus musculus (mouse) Single-cell RNA-seq 287 samples Submitted 2026/06/29 Platform GPL34328
At a glance

Single-cell RNA-seq · mouse · 287 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
287 samples: 68 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.
Summary (written by the submitters)
Radial glial progenitors (RGPs) produce all excitatory neurons in the developing cerebral cortex. Mosaic Analysis with Double Markers (MADM)-based lineage tracing in vivo revealed a quantitative framework of RGP lineage progression. Here we established MADM technology in embryonic stem cells to probe RGP lineage progression in self-organizing cortical organoid system.
Read the full summary ↓
We found that RGPs exhibit high level of equipotency in organoid rather than temporally stereotyped lineage progression as observed in vivo. RGPs in organoids showed increased lineage restriction, diminishing cell-type diversity in clones of cortical projection neurons, despite uniform RGP scRNAseq transcriptional signatures and unitary lineage trajectory. Hence, critical non-cell-autonomous cues absent in self-organizing systems and/or genuine stem cell niche are essential for faithful RGP lineage progression and the generation of cortical cell-type diversity.
This dataset
Open GSE327468 in finder → Find more mouse 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 GSE327468_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 PRJNA1451059 and SRA study SRP690480. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
68 conditions, mostly in triplicate
clone16 ×19 clone45 ×13 clone39 ×10 clone17 ×9 clone21 ×7 clone28 ×7 clone68 ×7 clone4 ×6 +61 more

Supports a between-group comparison across 286 samples.

How we read this: 68 replicated groups read from 287 sample titles; they account for 286 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

+ 247 more — browse all 287 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.