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Gene expression in mouse uterus during pre-implantation period after intrauterine administration treatment

GSE280930 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2024/12/09 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 20 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
20 samples: 5 × Uterus, 3.5dpc, control, vs 5 × Uterus, 3.5dpc, model, vs 5 × Uterus, 3.5dpc, model, PBMC, vs 5 × Uterus, 3.5dpc, model, PBMC-hCG,Groups 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
Intrauterine administration of peripheral blood mononuclear cells helps manage recurrent implantation failure by normalizing dysregulated gene expression including estrogen-responsive genes in miceCell communication and signaling : CCS · 2024
Summary (written by the submitters)
Intrauterine peripheral blood mononuclear cells (PBMC) therapy for recurrent implantation failure (RIF) has been reported to improve embryo implantation by acting on the endometrium. However, the exact mode of action of PBMC on the endometrium and the differences in the therapeutic effects of PBMC therapy with and without human chorionic gonadotropin (hCG) are unknown.
Read the full summary ↓
In this study, we investigated the changes in the endometrium during the implantation phase induced by PBMC administration and the differences in the efficacy of this therapy with and without hCG using a mouse model of implantation failure (IF).
Published in
Intrauterine administration of peripheral blood mononuclear cells helps manage recurrent implantation failure by normalizing dysregulated gene expression including estrogen-responsive genes in mice
Kitawaki Y, Horie A, Ikeda A et al. · Cell communication and signaling : CCS 2024 · PMID 39639317 · doi:10.1186/s12964-024-01904-3
This dataset
Open GSE280930 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 GSE280930_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: 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 PRJNA1171016 and SRA study SRP537578. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39639317) ↗
Study design
5 × Uterus, 3.5dpc, control, vs 5 × Uterus, 3.5dpc, model, vs 5 × Uterus, 3.5dpc, model, PBMC, vs 5 × Uterus, 3.5dpc, model, PBMC-hCG,

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 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
Datasets on a similar topic

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