DBA-Lectin Reactivity Defines Mouse Uterine Natural Killer Cell Subsets with Biased Gene Expression1
Bibliographic record
Abstract
Endometrial decidualization, a process essential for blastocyst implantation in species with hemochorial placentation, is accompanied by an enormous but transient influx of natural killer (NK) cells. Mouse uterine NK (uNK) cell subsets have been defined by diameter and cytoplasmic granule number, reflecting stage of maturity, and by histochemical reactivity with Periodic Acid Schiff (PAS) reagent with or without co-reactivity with Dolichos biflorus agglutinin (DBA) lectin. We asked whether DBA- and DBA+ mouse uNK cells were equivalent using quantitative RT-PCR analyses of flow-separated, midpregnancy (Gestation Day [gd] 10) cells and immunohistochemistry. CD3E (CD3)-IL2RB (CD122)+DBA cells were identified as the dominant Ifng transcript source. Skewed IFNG production by uNK cell subsets was confirmed by analysis of uNK cells from eYFP-tagged IFNG-reporter mice. In contrast, CD3E-IL2RB+DBA+ uNK cells expressed genes compatible with significantly greater potential for IL22 synthesis, angiogenesis, and participation in regulation mediated by the renin-angiotensin system (RAS). CD3E-IL2RB+DBA+ cells were further divided into VEGFA+ and VEGFA- subsets. CD3E-IL2RB+DBA+ uNK cells but not CD3E-IL2RB+DBA- uNK cells arose from circulating, bone marrow-derived progenitor cells by gd6. These findings indicate the heterogeneous nature of mouse uNK cells and suggest that studies using only DBA+ uNK cells will give biased data that does not fully represent the uNK cell population.
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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 | 0.001 |
| 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".