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Time-series single-cell transcriptomic profiling of luteal-phase endometrium uncovers dynamic characteristics and its dysregulation in recurrent implantation failures

GSE250130 Homo sapiens (human) Single-cell RNA-seq 28 samples Submitted 2024/11/18 Platform GPL29480
At a glance

Single-cell RNA-seq · human · 28 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
28 samples: 28 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.
Paper
Time-series single-cell transcriptomic profiling of luteal-phase endometrium uncovers dynamic characteristics and its dysregulation in recurrent implantation failuresNature communications · 2025
Summary (written by the submitters)
Understanding human endometrial dynamics remains a challenge, limiting early diagnosis and treatment of reproductive disorders. Here, we decoded the normal endometrial dynamics across the window of implantation (WOI) and its deficiency in endometrium from women with recurrent implantation failure (RIF) by analyzing data from over 220,000 cells, together with developing a computational model StemVAE that is capable of both temporal prediction and pattern discovery.
Read the full summary ↓
Our time-series atlas highlighted a two-stage stromal decidualization process whereas a gradual epithelial transitional process across the WOI allowing the identification of a time-varying gene set regulating epithelial receptivity. The epithelial receptivity genes were able to stratify the RIF endometrium into two classes of receptivity-deficiency involving early and late implantation. Further investigation uncovered epithelial cell dysfunction under hyperinflammatory microenvironment in RIF endometrium. The holistic characterization of physiological and pathophysiological WOI, and a computational tool trained on this temporal atlas provide a platform for future therapeutic developments.
Published in
Time-series single-cell transcriptomic profiling of luteal-phase endometrium uncovers dynamic characteristics and its dysregulation in recurrent implantation failures
Cao D, Liu Y, Cheng Y et al. · Nature communications 2025 · PMID 39747825 · doi:10.1038/s41467-024-55419-z
This dataset
Open GSE250130 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 GSE250130_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 PRJNA1051672 and SRA study SRP477651. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39747825) ↗
Study design
28 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 28 sample titles: 28 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
Datasets on a similar topic

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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.