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Single-nucleus profiling reveals a core disease signature and cell-type-specific vulnerabilities in early Rett syndrome

GSE301254 Mus musculus (mouse) Single-nucleus RNA-seq 32 samples Submitted 2026/05/06 Platform GPL24247

At a glance

Single-nucleus RNA-seq · mouse · 32 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: Mus musculus (mouse)
Samples: 32 samples: 8 × WT_M vs 8 × Null_M vs 8 × WT_F vs 8 × HET_FGroups 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-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndromeScience advances · 2026

Summary (written by the submitters)

Rett syndrome (RTT) is an X-linked neurological disorder caused by MECP2 mutations. Like other X-linked disorders, RTT patients have sex-specific differences in clinical presentation due to distinct cellular environments, where females have ~50% of cells expressing either a mutant or wild-type copy of MECP2 (mosaic) and males have 100% of cells expressing a mutant MECP2 (non-mosaic).

Read the full summary ↓

Typical RTT females have a short window of normal early development until ~6-18 months, followed by regression and progressive decline, whereas neonatal encephalopathy is more likely in RTT males. How these sex-specific differences in cellular context contribute molecularly to RTT pathogenesis, particularly in the presymptomatic stages of RTT females, remains poorly understood. Here, we profiled the hippocampal transcriptomes of female (Mecp2+/-) and male (Mecp2-/y) RTT mice at early timepoints using both bulk and single-nucleus RNA-seq, including sorted MeCP2 positive (MeCP2+) and MeCP2 negative (MeCP2-) neurons in female mice. We identified a core disease signature consisting of 12 genes consistently dysregulated only in MeCP2- cells across RTT models. Moreover, we uncovered non-cell-autonomous effects exclusively in female MeCP2+ excitatory neurons, but not inhibitory neurons, suggesting excitatory circuits are more vulnerable early in the mosaic RTT environment. The single-nuclei data also revealed that a previously underappreciated MeCP2- interneuron subtype had the most transcriptional dysregulation in both male and female RTT hippocampi. Together, these data highlight the different effects of MeCP2 loss on excitatory and inhibitory circuits between the mosaic and non-mosaic environment that appear early in RTT pathogenesis.

Published in

Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome

Li Y, Anderson AG, Qi G et al. · Science advances 2026 · PMID 42268975 · doi:10.1126/sciadv.aeb4265

This dataset

Open GSE301254 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 GSE301254_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 PRJNA1284340 and SRA study SRP596193. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42268975) ↗

Study design

8 × WT_M vs 8 × Null_M vs 8 × WT_F vs 8 × HET_F

Supports a between-group comparison across 32 samples.

How we read this: 4 replicated groups read from 32 sample titles; they account for 32 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

GSM9078845 WT_M_1 WT_M
GSM9078846 WT_M_2 WT_M
GSM9078847 WT_M_3 WT_M
GSM9078848 WT_M_4 WT_M
GSM9078849 Null_M_1 Null_M
GSM9078850 Null_M_2 Null_M
GSM9078851 Null_M_3 Null_M
GSM9078852 Null_M_4 Null_M
GSM9078853 WT_F_1 WT_F
GSM9078854 WT_F_2 WT_F
GSM9078855 WT_F_3 WT_F
GSM9078856 WT_F_4 WT_F
GSM9078857 HET_F_1 HET_F
GSM9078858 HET_F_2 HET_F
GSM9078859 HET_F_3 HET_F
GSM9078860 HET_F_4 HET_F
GSM9078861 WT_M_5 WT_M
GSM9078862 WT_M_6 WT_M
GSM9078863 WT_M_7 WT_M
GSM9078864 WT_M_8 WT_M
GSM9078865 Null_M_5 Null_M
GSM9078866 Null_M_6 Null_M
GSM9078867 Null_M_7 Null_M
GSM9078868 Null_M_8 Null_M
GSM9078869 WT_F_5 WT_F
GSM9078870 WT_F_6 WT_F
GSM9078871 WT_F_7 WT_F
GSM9078872 WT_F_8 WT_F
GSM9078873 HET_F_5 HET_F
GSM9078874 HET_F_6 HET_F
GSM9078875 HET_F_7 HET_F
GSM9078876 HET_F_8 HET_F

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