The whole is lesser than the sum of its parts? Dissecting layer-enriched samples of rodent placenta is worth the effort
Bibliographic record
Abstract
Gene expression in the placenta, assessed by bulk RNA-seq, is a common method to explore placental function. Many rodent studies homogenize the entire placenta, and yet doing so may obscure differences within specific functional regions such as the labyrinth, junctional zone and decidua. Conversely, analysis of the whole placenta could generate apparent differences due to changes in composition (e.g., relative amounts of labyrinth vs junctional zone) rather than differential gene expression. We assess the value of dissecting and separately analysing the labyrinth and junctional zone/decidua by comparing RNA-seq results from the labyrinth, junctional zone/decidua combined, and whole placenta from an experiment examining effects of maternal food restriction and fetal sex in C57BL6/J mice at gestational day 17.5. The number of genes identified as differentially expressed in response to maternal food restriction was substantially higher in the labyrinth (910 genes), than in the junctional zone/decidua (50 genes), which in turn was slightly higher than in the whole placenta (3 genes). Only one gene was differentially expressed in all 3 tissue types, and 20 genes were differentially expressed in both the labyrinth and junctional zone/decidua. The larger number of differentially expressed genes in the labyrinth was due to both larger effect sizes and estimates of effect sizes having smaller standard errors. While dissection to obtain layer-enriched samples is slightly more time-consuming than collection of whole placenta and requires some practice, our results show that layer-enrichment is clearly worth the effort.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".