Isolating and expanding endothelial progenitor cells from peripheral blood on peptide‐functionalized polystyrene surfaces
Bibliographic record
Abstract
The expansion of human peripheral blood endothelial progenitor cells to obtain therapeutically relevant endothelial colony-forming cells (ECFCs) has been commonly performed on xeno-derived extracellular matrix proteins. For cellular therapy applications, xeno-free culture conditions are desirable to improve product safety and reduce process variability. We have previously described a novel fluorophore-tagged RGD peptide (RGD-TAMRA) that enhanced the adhesion of mature endothelial cells in vitro. To investigate whether this peptide can replace animal-derived extracellular matrix proteins in the isolation and expansion of ECFCs, peripheral blood mononuclear cells from 22 healthy adult donors were seeded on RGD-TAMRA-modified polystyrene culture surfaces. Endothelial colony formation was significantly enhanced on RGD-TAMRA-modified surfaces compared to the unmodified control. No phenotypic differences were detected between ECFCs obtained on RGD-TAMRA compared to ECFCs obtained on rat-tail collagen-coated surfaces. Compared with collagen-coated surfaces and unmodified surfaces, RGD-TAMRA surfaces promoted ECFC adhesion, cell spreading, and clonal expansion. This study presents a platform that allows for a comprehensive in vitro evaluation of peptide-based biofunctionalization as a promising avenue for ex vivo ECFC expansion.
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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".