Gene-trap expression screening to identify endothelial-specific genes
Bibliographic record
Abstract
The endothelial cell is a key cellular component for blood vessel formation. Many signaling receptors expressed in endothelial cells play critical roles in vascular development during embryogenesis. However, downstream response genes required for vascular differentiation are still not clearly identified. Here we describe the development of a protocol for gene-trap expression screening in embryonic stem (ES) cells for endothelial-specific genes. ES cells were differentiated into endothelial cells on an OP9 feeder cell layer in 96-well plates. In a pilot screen, 5 gene-trapped ES cell lines showed an up-regulated expression of the gene trap lacZ reporter out of 864 ES clones screened. One of the trapped genes was endoglin, an endothelial-specific transforming growth factor-beta type III receptor, and another was ASPP1, a p53-binding protein. In vivo expression analysis of the lacZ reporter confirmed that both genes are specifically expressed in endothelial cells during early mouse embryogenesis. Gene-trap expression screening can thus be used to identify early endothelial-specific genes and analyze their function in mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".