Isolation-free Identification and Phenotyping of First-Trimester Extravillous Trophoblasts Residing in Cervical Fluid
Bibliographic record
Abstract
Abstract Preeclampsia (PE) remains difficult to predict, particularly when it manifests late in gestation. To capture early placental signals, we profiled trophoblast cells sampled from the cervix in the first trimester using mass cytometry (CyTOF). We established protocols for clinical sample storage and applied spike-in reference control cells to deliver reproducible, batch-corrected protein measurements, thereby advancing CyTOF from a discovery tool to a translational platform. Within HLA-G⁺CD45⁻ cells, we identified canonical CK7⁺ extravillous trophoblasts, as well as a previously unrecognized CK7⁻subset, and both subsets expressed placental proteins. Expression of PAPP-A, GAL-13, and GAL-14 was significantly altered in a pilot cohort of pregnancies that subsequently developed late-onset PE, distinguishing cases from controls at both single-marker and multivariate levels. These findings reveal unexpected trophoblast heterogeneity, demonstrate that placental alterations are detectable before the development of late-onset PE, and establish cervical trophoblast profiling as a promising platform for scalable biomarker discovery and first-trimester risk assessment in placenta-mediated disorders. Impact Statement First-trimester trophoblasts sampled from the cervix reveal early molecular changes associated with late-onset preeclampsia, while an isolation-free, reference-normalized CyTOF workflow establishes a scalable, clinically compatible platform for biomarker discovery and multicenter-ready early risk assessment in pregnancy.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".