Human endothelial colony forming cells (ECFCs) require endothelial protein C receptor (EPCR) for cell cycle progression and angiogenic activity
Bibliographic record
Abstract
Vascular repair and regeneration are critical for tissue homeostasis. Endothelial colony forming cells (ECFCs) are vessel-resident progenitors with vasoreparative capacity and they offer an important avenue for allogeneic cytotherapy to achieve perfusion of ischemic tissues. Endothelial Protein C Receptor (EPCR) has been proposed as a marker for vascular endothelial stem cells, but its precise role in ECFC biology remains unknown. The current study has investigated the biological relevance of EPCR in ECFC function. Our data show that over 95% of ECFCs exhibit high EPCR expression. These levels surpassing CD34 and CD157, positions EPCR as a new robust ECFC immunophenotypic marker, alongside established markers CD31 and CD105. Functionally, depleting EPCR expression in ECFCs significantly diminished angiogenic activity, including proliferation, migration and tube formation. This knockdown also altered normal ECFC barrier function. Transcriptomic analysis indicated that knockdown of EPCR led to enrichment of gene signatures for cell cycle, TGF beta, and focal adhesion kinases. G1 cell cycle arrest was confirmed in ECFCs with depleted EPCR. Mechanistically, EPCR knockdown led to increased release of TGFβ2 and SMAD2/3 activation, coupled with increased p21, decreased pFAK, and increased transgelin. Additionally, we showed that quiescent ECFCs showed significantly lower EPCR expression when compared to proliferating ECFCs. In agreement with this, cell sorting experiments demonstrated that ECFCs with the highest EPCR expression exhibited the highest clonogenic capacity. In summary, our findings highlight that EPCR expression in ECFCs is critical for their angiogenic activity, by modulating cell cycle progression.
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 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".