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Single-Cell Multiomic Atlas of Human Cortical Development in Down Syndrome - Neural Cells in vitro Basic Characterisation Bulk RNA-seq Dataset

GSE305135 Homo sapiens (human) Single-cell RNA-seq 45 samples Submitted 2025/12/23 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 45 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
45 samples: 14 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)
Down syndrome, caused by an extra copy of chromosome 21, is the most common genetic form of intellectual disability, which affects up to 1 in 700 live births. Yet, how increased dosage of the ~200 protein-coding genes on human chromosome 21 affects cortical development and function remains unclear. Here we generated a single-cell transcriptome and chromatin accessibility atlas of the human foetal cortex at mid gestation (11-20 weeks after conception), a critical period of cortical development.
Read the full summary ↓
We uncovered an early global transcriptional network disruption, subtly altering ~1000 genes involved in neural development and function primarily in excitatory neurons. These changes reflected clinical phenotypes like intellectual disability and epilepsy, were accompanied by a significant reduction in layer 4 neurons, and were driven by a network of transcription factors including chromosome 21 genes BACH1, PKNOX1, and GABPA. Finally, a xenograft model replicates key molecular features, offering an experimental platform for validating new therapeutic targets. This resource defines the gene-regulatory landscape of the developing human cortex in Down syndrome, revealing the earliest known molecular and cellular signatures of its neurological manifestations and novel candidate targets for therapeutic interventions.
This dataset
Open GSE305135 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 GSE305135_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 PRJNA1304490 and SRA study SRP608140. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 conditions, mostly in triplicate
iPSC-derived neural progenitors, trisomy 21, … ×4 iPSC-derived neural progenitors, trisomy 21, … ×4 iPSC-derived neural progenitors, disomic, Cel… ×4 iPSC-derived neural progenitors, disomic, Cel… ×4 iPSC-derived neurons, trisomy 21, Cell line C… ×3 iPSC-derived neural progenitors, trisomy 21, … ×3 iPSC-derived neurons, disomic, Cell line C9, … ×3 iPSC-derived neural progenitors, disomic, Cel… ×3 +6 more

Supports a between-group comparison across 45 samples.

How we read this: 14 replicated groups read from 45 sample titles; they account for 45 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

+ 5 more — browse all 45 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.