A single-cell transcriptomic atlas of the pigtail macaque placenta in late gestation
Bibliographic record
Abstract
BACKGROUND: The placenta is a complex organ with multiple immune and non-immune cell types that promote fetal tolerance and facilitate the transfer of nutrients and oxygen. The nonhuman primate (NHP) is a key experimental model for studying human pregnancy complications, in part due to similarities in placental structure, which makes it essential to understand how single-cell populations compare across the human and NHP maternal-fetal interface. We constructed a single-cell RNA-Seq (scRNA-Seq) atlas of the placenta from the pigtail macaque (Macaca nemestrina) in the third trimester, comprising three different tissues at the maternal-fetal interface: the chorionic villi (placental disc), chorioamniotic membranes, and the maternal decidua. Each tissue was separately dissociated into single cells and processed through the 10X Genomics and Seurat pipeline, followed by aggregation, unsupervised clustering, and cluster annotation. Next, we determined the maternal-fetal origins of cell populations and analyzed single-cell RNA trajectory, Gene Ontology enrichment, and cell-cell communication. RESULTS: Single-cell populations in the pigtail macaque were strikingly similar in their identity and frequency to those found in the human placenta, including cells from trophoblast, stromal cell, immune, and macrophage lineages. An advantage of our approach was the deep sequencing of three tissues at the maternal-fetal interface, which yielded a rich diversity of common and rare single-cell populations. CONCLUSIONS: Single-cell populations from the third-trimester pigtail macaque placenta are highly concordant with those from the human placenta. This scRNA-Seq atlas provides a powerful resource for understanding experimental perturbations on the NHP placenta.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".