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Diploid embryo complementation of embryonic stem cells efficiently generates a homozygous Gjb2 mutant deaf mouse model

GSE271800 Mus musculus (mouse) RNA-seq (gene expression) 131 samples Submitted 2026/07/01 Platform GPL24247Platform GPL28330Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 131 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
131 samples: 6 conditions, mostly with about 9 replicates eachGroups 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)
Our work demonstrates that generating all ESC-derived mice via diploid embryo complementation is more efficient and feasible than the tetraploid approach. The broad application of this method will significantly accelerate the construction of disease models and xenogeneic chimeras using stem cell technology. Besides, our results indicate that the cell fate plasticity of eight-cell embryos is both flexible and variable.
Read the full summary ↓
This plasticity is influenced by material exchange, through which embryonic stem cells transmit information, similar to the transmission of information from the second polar body. The efficient generation of Gjb235delG/35delG mice via diploid complementation will accelerate the understanding of pathogenic mechanisms and the investigation of therapeutic effects for hereditary deafness. Additionally, other disease models with GJB2 pathogenic mutations (235delC, V27I, R75W, etc.) can also be efficiently produced using this method for pre-clinical research
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 GSE271800_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1133646 and SRA study SRP518729. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly with about 9 replicates each
RFP ×55 GFP ×40 RFPpos ×9 RFPpos-GFPpos ×9 GFPpos ×8 35G-deep ×6 MEF-p ×1 Gjb2-35G-p ×1 +2 more

Supports a between-group comparison across 127 samples.

How we read this: 6 replicated groups read from 131 sample titles; they account for 127 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

+ 91 more — browse all 131 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.