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Single-Cell Multiomic Atlas of Human Cortical Development in Down Syndrome - Fetal Cortex Multiome Dataset

GSE305146 Homo sapiens (human) Single-cell RNA-seq 92 samples Submitted 2025/12/23 Platform GPL30173Platform GPL24676
At a glance

Single-cell RNA-seq · human · 92 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
92 samples: 92 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.
Paper
Single-cell atlas of the developing Down syndrome brain cortexNature medicine · 2026
Summary (written by the submitters)
Down syndrome, caused by an extra copy of chromosome 21, is the most common genetic form of intellectual disability affecting up to 1 in 700 live births1-3. Yet, it remains unclear how the increased dosage of ~200 protein-coding genes on chromosome 21 affects brain development, particularly in the cerebral cortex—the area central to higher-level cognitive functions4-7.
Read the full summary ↓
Here we generated a single-cell transcriptome and chromatin accessibility atlas from 30 human fetal cortical samples at mid gestation (10-20 weeks after conception), a critical period of cortical development8. We discovered an early global transcriptional network disruption, subtly altering ~600 genes involved in neural development and function, mostly in excitatory neurons accompanied by a significant reduction in RORB/FOXP1 expressing excitatory neurons. Multimodal network analyses predicted the chromosome 21 transcription factors BACH1, PKNOX1, and GABPA as key regulatory hubs controlling dozens of genes genetically linked to intellectual disability. Antisense-mediated normalization of the increased activity of these transcription factors in stem-cell-derived neural cells in vitro, which partially recapitulated molecular changes in the Down syndrome cortex, rescued expression of several of these predicted intellectual disability-associated targets. Finally, a humanized in vivo model replicates key molecular features of Down syndrome not recapitulated in neural cells in vitro. This resource defines the gene-regulatory landscape of the developing human cortex in Down syndrome, revealing early molecular and cellular signatures and candidate therapeutic targets, along with a human in vivo experimental platform for their preclinical testing.
Published in
Single-cell atlas of the developing Down syndrome brain cortex
Lattke M, Tan WL, Sukumaran SK et al. · Nature medicine 2026 · PMID 41545595 · doi:10.1038/s41591-026-04211-1
This dataset
Open GSE305146 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 GSE305146_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 PRJNA1304509 and SRA study SRP608209. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41545595) ↗
Study design
92 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 92 sample titles: 92 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

+ 52 more — browse all 92 samples with per-sample file links →

Datasets on a similar topic

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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.