GEO series

STAT3 signalling drives tissue expansion during postimplantation mouse development [scRNA-seq]

GSE259342 Mus musculus (mouse) Single-cell RNA-seq 16 samples Submitted 2025/04/07 Platform GPL21103

At a glance

Single-cell RNA-seq · mouse · 16 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 16 samples: 16 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.

Summary (written by the submitters)

STAT3 has been studied extensively in the context of self-renewal of naïve pluripotent mouse embryonic stem cells (mESCs). Although STAT3 is required to maintain ICM lineages when maternally eliminated, the role of STAT3 during gastrulation is unclear as the conventional knockout of Stat3 develops until E6.5. In this study, we observed that zygotic loss of Stat3 on CD1 genetic background leads to consistent developmental retardation from implantation to mid-gestation, beginning with a significant reduction in the number of epiblast cells by the time of implantation.

Read the full summary ↓

Remarkably, mutants appear to scale normally and resemble non-affected embryos from the previous day at all postimplantation stages examined. Bulk RNA-seq analysis using isolated epiblast cells revealeed that E6.5 null epiblasts were intermediate between WT E5.5 and E6.5; E7.5 null epiblasts grouped more closely with WT E6.5 than E7.5 epiblasts. Gene ontology analysis revealed lipid metabolism was compromised in Stat3 null embryos as early as E6.5. Interestingly, Socs3, downstream target of STAT3, was not induced in WT rpiblast cells until E7.5. We found Y705 phospholylated STAT3 was not exist postimplantation epiblast, but S727 phoshorylated STAT3 was abundant over the time, suggesting postimplantation specific roles of S727 phosphorylated STAT3 in growth and differentiation. Our study revealed that while Stat3 null epiblast cells progress along a near normal differentiation trajectory, Stat3 deletion compromises lipid metabolism as early as E6.5.

This dataset

Open GSE259342 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 GSE259342_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 PRJNA1081299 and SRA study SRP491969. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

16 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 16 sample titles: 16 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

GSM8114469 E8.5 rep1 Stat3null
GSM8114470 E9.5 rep3 Stat3null
GSM8114471 E8.5 rep1 WT
GSM8114472 E9.5 rep3 WT
GSM8114473 E9.5 rep4 Stat3null
GSM8114474 E9.5 rep4 WT
GSM8114475 E7.5 rep1 Stat3null
GSM8114476 E9.5 rep1 Stat3null
GSM8114477 E7.5 rep1 WT
GSM8114478 E9.5 rep1 WT
GSM8114479 E7.5 rep2 Stat3null
GSM8114480 E8.5 rep2 Stat3null
GSM8114481 E9.5 rep2 Stat3null
GSM8114482 E7.5 rep2 WT
GSM8114483 E8.5 rep2 WT
GSM8114484 E9.5 rep2 WT

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