Synergistic Multi-Peptide Interfaces Enhance Early Osteogenic Differentiation of Mesenchymal Stem Cells
Bibliographic record
Abstract
The extracellular matrix (ECM) orchestrates stem cell fate through a sophisticated interplay of biochemical and biophysical cues. While prior biomaterial strategies have typically employed one or two bioactive peptides, such approaches rarely replicate the multifaceted signaling environment of native ECM. Here, we present a biomaterial surface cofunctionalized with three distinct peptides─BMP2, RGD, and P15─through a novel spin-coating silanization strategy, providing an advanced level of ECM biomimicry. Surface functionalization was confirmed via polarization modulation-infrared reflection-absorption spectroscopy (PM-IRRAS) and fluorescence microscopy. Human mesenchymal stem cells (hMSCs) cultured on these multipeptide surfaces were systematically evaluated by RT-qPCR and immunocytochemistry to assess the expression of osteogenic markers at the gene and protein levels. Our results demonstrate that the concurrent presentation of BMP2, RGD, and P15 significantly accelerates early osteogenic gene expression and enhances the sustained differentiation of hMSCs compared to single- or dual-peptide modifications. These findings highlight the importance of multifactorial signaling for directing stem cell fate and establish multifunctionalized surfaces as promising platforms for improving biomaterial performance in regenerative medicine.
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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".