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 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) | 0.000 | 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 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".