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Microengineered Patient-derived Endometrium-on-a-Chip for the Evaluation of Endometrial Receptivity and Personalized Translational Medicine

GSE307510 Homo sapiens (human) RNA-seq (gene expression) 10 samples Submitted 2025/10/01 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 10 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
Homo sapiens (human)
Samples
10 samples: 3 × RIF endometrium tissue vs 3 × RIF endometrium-on-a-chip vs 2 × Normal endometrium tissue vs 2 × Normal endometrium-on-a-chipGroups 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
Microengineered patient-derived endometrium-on-a-chip for the evaluation of endometrial receptivity and personalised translational medicineNature communications · 2025
Summary (written by the submitters)
Endometrial receptivity plays a pivotal role in determining the success of embryo implantation and supports early pregnancy development, playing a central role in species propagation. However, current methods for assessing endometrial receptivity lack robust evidence-based validation, and reliable technique for accurately evaluating this process in patients with infertility are lacking.
Read the full summary ↓
In this study, we introduced a novel patient-derived vascularised endometrium-on-a-chip (EoC) model, comprising three distinct layers, namely the epithelium, stroma, and blood vessels. This EoC was cross-species validated using both mouse and human samples, successfully replicating the dynamic microenvironment and mimicking both temporal and spatial features of the endometrial tissue. Using our EoC, we developed a clinically relevant endometrial receptivity scoring system, ERS², based on the molecular expression profiles of crucial endometrial receptivity markers and the morphological analyses of endometrial angiogenesis. The ERS² enables the personalised analysis of endometrial health and the potential for embryo implantation, addressing the individual variations in endometrial biology that may not be captured by traditional diagnostic methods. By leveraging our EoC, the ERS² replicate the physiological characteristics of the endometrium and provided personalised, patient-specific analyses. While larger-scale validation is required before clinical implementation, this highlights its significant potential for identifying therapeutic candidates tailored to individual patient, advancing the prospects for personalised reproductive care. Furthermore, progressive improvements in the patient’s endometrial microenvironment were observed following platelet-rich-plasma treatments, and the optimal timing for embryo transfer was identified in patients with unexplained repeated implantation failure using the EoC-ERS². This highlights the practical utility of our model not only for assessing individual receptivity but also for evaluating the personalised efficiency of interventions aimed at improving implantation outcomes. This study represents the first use of a microengineered EoC model and its scoring system to assess endometrial receptivity and predict successful pregnancy outcomes, offering new insights into personalised infertility treatments and advancing the potential of targeted therapies in reproductive medicine.
Published in
Microengineered patient-derived endometrium-on-a-chip for the evaluation of endometrial receptivity and personalised translational medicine
Lee G, Lee YG, Koo HS et al. · Nature communications 2025 · PMID 41290570 · doi:10.1038/s41467-025-65406-7
This dataset
Open GSE307510 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 GSE307510_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 PRJNA1321904 and SRA study SRP618445. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41290570) ↗
Study design
3 × RIF endometrium tissue vs 3 × RIF endometrium-on-a-chip vs 2 × Normal endometrium tissue vs 2 × Normal endometrium-on-a-chip

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 10 sample titles; they account for 10 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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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.