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MECP2 Mutations Rewire Human ESC Fate and Bias Cortical Lineage Commitment [RNA-Seq]

GSE303838 Homo sapiens (human) Single-cell RNA-seq 48 samples Submitted 2026/04/23 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 48 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
48 samples: 16 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.
Paper
MECP2 mutations rewire human ESC fate and bias cortical lineage commitmentStem cell reports · 2026
Summary (written by the submitters)
Rett syndrome arises from loss-of-function mutations in the X-linked chromatin regulator MECP2, yet the earliest molecular derailments in human development remain poorly defined. Using isogenic hESC models carrying three patient-derived MECP2 mutations, we followed the transcriptome from pluripotency through neuro-ectoderm, neural stem, and neural progenitor stages and into four-month cerebral organoids.
Read the full summary ↓
Stage dominated transcriptional variance, but mutants shared a secondary program enriched for synaptic-membrane and extracellular-matrix genes. Single-cell profiling revealed a naïve-like, hyper-proliferative state marked by up-regulation of ZFP42 at ESC stage. Strikingly, EMX1, a cortical radial-glia determinant, was consistently suppressed from the earliest stage onward, and cerebral organoids subsequently generated fewer excitatory neurons in favour of inhibitory and glial lineages. These data chart a continuous developmental trajectory for MECP2-mutant human cells and nominate ZFP42 and EMX1 dysregulation as tractable entry points for dissecting Rett pathogenesis.
Published in
MECP2 mutations rewire human ESC fate and bias cortical lineage commitment
Guillon M, Brin M, Gabet E et al. · Stem cell reports 2026 · PMID 42030940 · doi:10.1016/j.stemcr.2026.102895
This dataset
Open GSE303838 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 GSE303838_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 PRJNA1297746 and SRA study SRP604083. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42030940) ↗
Study design
16 conditions, mostly in triplicate
hESC, d0, MECP2-R133C, ×3 hESC, d0, MECP2-R168X, ×3 hESC, d0, MECP2-R270X, ×3 hESC, d0, MECP2-WT, ×3 Neuroectoderm, d3, MECP2-R133C, ×3 Neuroectoderm, d3, MECP2-R168X, ×3 Neuroectoderm, d3, MECP2-R270X, ×3 Neuroectoderm, d3, MECP2-WT, ×3 +8 more

Supports a between-group comparison across 48 samples.

How we read this: 16 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

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