Spontaneous fibroblast‐derived pericyte recruitment in a human tissue‐engineered angiogenesis model in vitro
Bibliographic record
Abstract
Cooperation between endothelial cells and pericytes is essential to the stabilization and maturation of blood microvessels. We developed a unique in vitro tissue-engineered model to study angiogenesis. The human endothelialized reconstructed connective tissue model promotes the formation of a three-dimensional branching network of capillary-like tubes (CLT) with closed lumens. The purpose of this work was to investigate whether pericytes were spontaneously recruited around CLT in the model. We demonstrated that smooth muscle α-actin (SMA)-positive cells were found closely associated with PECAM-1-positive capillaries in the model. Twelve percent (±2.6) of SMA-positive cells were detected along with 15% (±1.64) von Willebrand factor-positive endothelial cells in the culture system after 31 days of in vitro maturation. Conversely, no SMA-positive cells were detected in reconstructed connective tissues made solely of fibroblasts. Knowing that PDGF is a major factor in the recruitment of pericytes, we showed that blockade of the PDGFB receptor using the inhibitor AG1296 induced an overall 5, 2.6, and 2.4-fold decrease in the SMA-positive cells, von Willebrand factor-positive cells, and number of capillaries, respectively. Using combinations of human GFP-positive fibroblasts and endothelial cells, we demonstrated that pericytes were recruited from the fibroblast population in the model. In conclusion, our tissue-engineered culture system promotes the spontaneous formation of a network of capillaries and the recruitment of pericytes derived from fibroblasts. Since pericytes are essential components of the blood microvasculature, this culture system is a powerful model to study angiogenesis and endothelial cell/pericyte interactions in vitro.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".