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Integrative single-nucleus multiomic, CUT&Tag, and bulk RNA-seq profiling of human and mouse prefrontal cortex reveals gene regulatory mechanisms of cortical development and Meis2 function

GSE325427 Homo sapiens (human) Single-nucleus RNA-seq 34 samples Submitted 2026/07/15 Platform GPL24676Platform GPL24247
At a glance

Single-nucleus RNA-seq · human · 34 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism
Homo sapiens (human)
Samples
34 samples: 8 conditions, mostly in duplicateGroups 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 development of the prefrontal cortex (PFC) is governed by coordinated transcriptional and epigenomic regulatory programs. To comprehensively characterize these processes, we generated an integrative multi-modal dataset combining single-nucleus multiomic (snRNA-seq and snATAC-seq), CUT&Tag, and bulk RNA-seq data from human and mouse PFC.
Read the full summary ↓
Single-nucleus multiomic data from human mid-fetal dorsolateral prefrontal cortex (dlPFC) samples and mouse postnatal day 1 (P1) medial prefrontal cortex (mPFC) and motor cortex (MC) and provide matched gene expression and chromatin accessibility profiles across diverse cell types. Complementary CUT&Tag experiments profiling key transcription factors (RARA, RARB, RXRG, MEIS2) and histone modifications (H3K27ac, H3K4me3) further define regulatory elements and transcriptional control landscapes. To investigate the functional role of Meis2, bulk RNA-seq was performed on mouse P0 medial prefrontal cortex (mPFC) from control and Meis2 conditional knockout (cKO) samples. These data enable the identification of gene expression changes associated with Meis2 perturbation. Together, this integrated dataset provides a resource for studying gene regulatory networks underlying cortical development and for linking transcription factor activity to downstream gene expression programs across species.
This dataset
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.

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 GSE325427_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1438418 and SRA study SRP684386. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in duplicate
RNA-seq of CTR mouse P0 mPFC sample MP0PFC ×3 RNA-seq of Meis2-cKO mouse P0 mPFC sample MP0… ×3 H3K27ac CUT&Tag of human mid-fetal dlPFC DFC ×2 H3K4me3 CUT&Tag of human mid-fetal dlPFC DFC ×2 MEIS2 CUT&Tag of human mid-fetal dlPFC DFC ×2 RARA CUT&Tag of human mid-fetal dlPFC DFC ×2 RARB CUT&Tag of human mid-fetal dlPFC DFC ×2 RXRG CUT&Tag of human mid-fetal dlPFC DFC ×2 +16 more

Supports a between-group comparison across 18 samples.

How we read this: 8 replicated groups read from 34 sample titles; they account for 18 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
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