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Transcriptional profiling defines unique subtypes of transit amplifying neural progenitors within the neonatal mouse subventricular zone

GSE309574 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2025/09/30 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 20 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
20 samples: 5 conditions, mostly with about 4 replicates eachGroups 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
Transcriptional Profiling Defines Unique Subtypes of Transit Amplifying Neural Progenitors Within the Neonatal Mouse Subventricular ZoneBiomolecules · 2025
Summary (written by the submitters)
While significant progress has been made in understanding the heterogeneity of Neural Stem Cells (NSCs), our understanding of similar heterogeneity among the more abundant transit amplifying progenitors is lagging. Our work on the neural progenitors (NPs) of the neonatal subventricular zone (SVZ) began over a decade ago, when we used antibodies to the 4 antigens, CD133, LeX, CD140a, and NG2 to perform FACs to classify subsets of the neonatal SVZ as either multi-potential (MP1, MP2, MP3, MP4 and PFMPs), glial-restricted (GRP1, GRP2, and GRP3) or neuron-astrocyte restricted (BNAP).
Read the full summary ↓
Using RNAseq, we have characterized the distinctive molecular fingerprints of 4 SVZ neural progenitors and compared their gene expression profiles to those of the NSCs. We performed bioinformatic analyses to provide insights into each NP type's unique interactome and the transcription factors regulating their development. Overall, we identified 1581 genes upregulated in at least one NP compared to the NSCs. Of these genes, 796 genes were upregulated in BNAP/GRP1 compared to NSCs; 653 in GRP2/MP3; 440 in GRP3; and 527 in PFMPs. One gene in particular that emerged from our analysis that can be used to distinguish the NPs from the NSCs is Etv1, also known as Er81. Also notable is that the neural stem cells downregulated cilia formation genes as they differentiated to become multipotential progenitors. Among the NPs, both PFMP and GRP3 subtypes differentially expressed genes related to neuron and oligodendrocyte development, including Matn4, Lhfpl3 and Olig2. GRP3s uniquely expressed Etv5, a transcription factor known to promote glial cell fate specification, while PFMPs uniquely expressed Lhx6, a transcription factor that regulates interneuron specification. PFMPs also expressed transcripts for olfactory receptors. Unlike the other NPs, the GRP1 and GRP2 NPs differentially expressed genes for proteins involved in immune function. The present work will serve as an important resource for investigators interested in further defining the transit amplifying progenitors of the mammalian SVZ.
Published in
Transcriptional Profiling Defines Unique Subtypes of Transit Amplifying Neural Progenitors Within the Neonatal Mouse Subventricular Zone
Zaritsky R, Kumari E, Velloso FJ et al. · Biomolecules 2025 · PMID 41154668 · doi:10.3390/biom15101438
This dataset
Open GSE309574 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 GSE309574_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 PRJNA1336281 and SRA study SRP629589. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41154668) ↗
Study design
5 conditions, mostly with about 4 replicates each
BNAPGRP1 ×4 GRP2MP3 ×4 GRP3 ×4 PFMP ×4 NSC ×4

Supports a between-group comparison across 20 samples.

How we read this: 5 replicated groups read from 20 sample titles; they account for 20 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
Datasets on a similar topic

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