Cell type and region-specific transcriptional changes in the endometrium of women with RIF identify potential treatment targets
Bibliographic record
Abstract
Recurrent implantation failure (RIF) is a devastating condition that leaves many undergoing fertility treatment childless. The human endometrium is receptive to a blastocyst for a brief period, the window of implantation. Critical knowledge underpinning biological processes leading to RIF, essential for effective treatment, is lacking. We employed spatial transcriptomics to define region- and cell-type-specific differences in endometrial gene expression in luteinizing hormone timed biopsies between women with RIF (n = 8) and fertile controls (FC) (n = 8). Differentially expressed genes (DEGs) were identified when comparing endometrial regions between FC and RIF (685 luminal epithelium, 293 glandular epithelium, 419 subluminal stroma, 264 functionalis stroma, 1,125 subluminal stromal CD45 + leukocytes, and 1,049 functionalis stromal CD56 + leukocytes). Only 57 DEGs were common to all subregions and cell types, which highlights that multiple DEGs are lost when the endometrium is examined as a single entity. When RIF-specific DEGs were leveraged against knowledge from mouse genetic models, genes associated with aberrant embryo implantation phenotypes were observed, mostly in immune cell populations. Dysregulated pathways in specific endometrial regions included the “WNT signaling pathway,” altered in the functionalis and subluminal stroma. “Response to estradiol” and “ovulation cycle” pathways were dysregulated in the subluminal stroma. In silico drug screening identified potential compounds that can reverse the RIF gene expression profile (e.g., raloxifene, bisoprolol). Our findings, in a well-characterized cohort, highly endorse consideration of each endometrial region and cell type as separate entities. Ignoring individual regions and composite cell populations will overlook important aberrations, forego potential treatment targets, and lead to research waste pursuing clinically irrelevant treatment options.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".