Non-integrating direct reprogramming generates therapeutic endothelial cells with sustained vascular regeneration capacity enhanced by nanomatrix delivery
Bibliographic record
Abstract
Rationale: Direct reprogramming of fibroblasts into endothelial cells (rECs) using ETV2 shows promise for vascular regeneration. However, current approaches using integrating viral vectors pose clinical translation barriers, and poor long-term cell survival limits therapeutic efficacy. Objective: To develop a clinically compatible method for generating rECs using non-integrating adenoviral ETV2 (Ad-ETV2) and enhance their engraftment and therapeutic efficacy through peptide amphiphile (PA) nanomatrix encapsulation. Methods and Results: Human dermal fibroblasts were reprogrammed using Ad-ETV2 and characterized by flow cytometry, RNA sequencing, and functional assays. Therapeutic efficacy was evaluated in murine hindlimb ischemia with or without PA-RGDS encapsulation over 12 months. Ad-ETV2 induced robust endothelial gene expression (CDH5, KDR, PECAM1) within 6 days, with 40-50% reprogramming efficiency. KDR+ Ad-rECs demonstrated functional endothelial properties including Ac-LDL uptake, tube formation, and exceptional proangiogenic factor secretion (200-fold higher HGF than HUVECs). RNA sequencing revealed rapid transcriptional reprogramming with fibroblast gene suppression and endothelial/angiogenic gene activation. In hindlimb ischemia, Ad-rECs significantly enhanced blood flow recovery and capillary density versus controls. Long-term analysis revealed sustained vascular contribution through three mechanisms: direct incorporation, perivascular support, and vessel guidance, persisting throughout 12 months-the longest reported follow-up for reprogrammed cells. PA-RGDS encapsulation markedly improved cell retention; while 75% of cells were lost by 3 months, retention stabilized thereafter with minimal additional loss through 12 months. Conclusions: Adenoviral ETV2 delivery enables efficient generation of clinically compatible rECs without genomic integration. These cells demonstrate potent and sustained therapeutic efficacy through multiple vascular regeneration mechanisms. PA-RGDS encapsulation significantly enhances long-term engraftment, establishing this combined approach as a promising platform for treating ischemic cardiovascular diseases.
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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 | 0.000 | 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) | 0.001 | 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".