Uncovering the immune landscape of uterus-draining lymph nodes in human pregnancy 9249
Bibliographic record
Abstract
Abstract Description Immunological tolerance is fundamental to the maintenance of human health, where its aberrant induction or loss has the potential to issue life-threatening consequences. During pregnancy, maternal tolerance to the genetically semi-foreign fetus is instrumental for delivery of a healthy infant. Despite this known requirement, the processes that establish immune tolerance to fetal alloantigen remain incompletely understood. Research in animal models have illustrated that immune tolerance arises through a spatially coordinated process involving lymph nodes (LNs); however, uterus-draining (ud) LNs remain understudied in the context of pregnancy. Here, we employed a 477-gene panel to construct a spatial transcriptomic atlas of udLNs during human pregnancy. Our analysis revealed an enrichment of a CD8+ Tex-like cells and a sinusoidal macrophage subset characterized by upregulated expression of immunoregulatory genes (CD163, CD5L, etc.) and the erythropoietin receptor (EPOR). Stimulation of human PBMCs with extravillous trophoblast-conditioned media induced EPOR expression on myeloid cells, demonstrating that the placental secretome can equip myeloid cells with immunoregulatory properties. In EPOR-reporter mice, we observed a similar macrophage subset emerge in pregnant mice. This has prompted ongoing experiments in which EPOR is conditionally knocked out in mature myeloid cells to deconvolute how EPOR+ macrophages modulate maternal-fetal immune tolerance and pregnancy outcomes. Funding Sources Supported by Stanford Maternal and Child Health Research Institute; Canadian Institutes of Health Research 187865. Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)
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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 | 0.001 | 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".