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Glandular Epithelium and Unexplained Recurrent Implantation Failure

GSE248784 Homo sapiens (human) Single-cell RNA-seq 21 samples Submitted 2026/05/20 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 21 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
21 samples: 21 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
Endometrial Epithelial Lactate Deficiency Drives CD8(+) T-Cells Dysregulation in Unexplained Recurrent Implantation FailureAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
Summary (written by the submitters)
Recurrent Implantation Failure (RIF), which refers to the failure to achieve clinical pregnancy following embryo transfer repetitively, continues to be a major obstacle in assisted reproductive technology. RIF could be caused by many disease and most of which is related to abnormal endometrium. However, unexplained RIF is a special situation which puzzles doctors and patients.
Read the full summary ↓
We use single-cell RNA sequencing to investigate the endometrial mechanism in unexplained RIF because the complicated environment of endometrium. Our results showed endometrium of RIF had higher levels of proliferation and activation of cytotoxic CD8+ T cells, which reject embryo implantation. Furthermore, glandular epithelium exhibits the highest metabolic activity, and it showed abnormal glycolysis and lower lactic acid production in the RIF, which was characterized by lower expression of genes related to glucose intake (SLC12A1), glucose metabolism (ALDOA), lactic acid production (LDHA) and output (SLC16A4) compared with the counterpart in the normal. The suppressive effect of lactic acid on CD8+ T cells was verified in vivo and vitro. We propose that RIF endometrium has not established the immune tolerance to embryo, which was attributed the abnormal glycolysis and lower lactic acid production in glandular epithelium.
Published in
Endometrial Epithelial Lactate Deficiency Drives CD8(+) T-Cells Dysregulation in Unexplained Recurrent Implantation Failure
He Y, Ke K, Meng Q et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2026 · PMID 41758694 · doi:10.1002/advs.202524090
This dataset
Open GSE248784 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 GSE248784_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 PRJNA1046008 and SRA study SRP474790. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41758694) ↗
Study design
21 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 21 sample titles: 21 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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