GEO series

Diversity of Cortical Progenitors Directs Neuronal Layer Formation and Regional Glial Patterning

GSE319368 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2026/02/12 Platform GPL34328

At a glance

RNA-seq (gene expression) · mouse · 10 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: Mus musculus (mouse)
Samples: 10 samples: 5 × GFAP vs 5 × NG2Groups 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: Diversity of cortical progenitors directs neuronal layer formation and regional glial patterningStem cell reports · 2026

Summary (written by the submitters)

How distinct cortical progenitor types and their temporal differences influence neuronal and glial development remains a key question in neurobiology. While Radial Glial Cells (RGCs) are well-established as Neural Progenitors (NPCs), NG2+NPCs have emerged as contributors with unique characteristics. Here, we combined in utero electroporation, lineage tracing and transcriptomic profiling to compare lineage outputs of NG2+ and GFAP+NPCs in the developing mouse cortex.

Read the full summary ↓

Both generate neurons and glia, but with partially distinct patterns in lineage output, spatial distribution, and fate bias. NG2+NPCs undergo an early neurogenic phase followed by glial differentiation, producing glia with laminar distribution biases. In contrast, GFAP+NPCs sustain neurogenesis longer, producing a broader range of neurons and more constant glia during development. Our findings highlight the contribution of progenitor identity and temporal differences to distinct, partially overlapping lineage trajectories, supporting a model in which fate potential and timing co-regulate the spatial and laminar organization of cortical cell types.

Published in

Diversity of cortical progenitors directs neuronal layer formation and regional glial patterning

Ojalvo-Sanz AC, Figueres-Oñate M, Barriola S et al. · Stem cell reports 2026 · PMID 41861816 · doi:10.1016/j.stemcr.2026.102855

This dataset

Open GSE319368 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 GSE319368_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 PRJNA1423174 and SRA study SRP676751. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41861816) ↗

Study design

5 × GFAP vs 5 × NG2

Supports a between-group comparison across 10 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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

GSM9516752 GFAP-1 GFAP
GSM9516753 GFAP-2 GFAP
GSM9516754 GFAP-3 GFAP
GSM9516755 GFAP-4 GFAP
GSM9516756 GFAP-5 GFAP
GSM9516757 NG2-1 NG2
GSM9516758 NG2-2 NG2
GSM9516759 NG2-3 NG2
GSM9516760 NG2-4 NG2
GSM9516761 NG2-5 NG2

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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.