Endothelial tip cells in culture: An in vitro approach for studies of angiogenesis
Bibliographic record
Abstract
Growing blood vessel sprouts are composed of specific subtypes of endothelial cells. In short, a blood vessel sprout is led by a tip cell that is followed by stalk cells and phalanx cells. Because of their leading role in vessel sprouts and their unique protein expression, tip cells form an attractive selective target for anti-angiogenic therapy. Consequently, endothelial tip cells are subject of many pre-clinical studies that aim to identify specific targets for novel therapies to treat retinal diseases. Thus far, most of the research on tip cells has been performed in animal models to identify possible selective molecular therapeutic targets in tip cells. In order to contribute to a reduction in the number of laboratory animals, we have developed an in vitro model to study tip cells. The Ocular Angiogenesis Group AMC has shown for the first time that tip cells can be identified in endothelial cell cultures by expression of the protein CD34. In the present thesis, this in vitro tip cell model was used to further characterize tip cells with respect to the growth factors and pathways involved. The studies presented in this thesis describe 3 approaches to study tip cells in vitro: • Identification of endothelial tip cell-specific proteins, • Effects of angiogenesis inhibitors on endothelial tip cells in vitro, and • Identification of interactions between endothelial tip cells, non-tip cells and extracellular proteins. This thesis shows that tip cells in cultures of human endothelial cells are a versatile model that can be employed for different aspects of tip cells research and has several advantages over the use of animal models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".