Manipulating Stem Cell Fate with Disordered Bioactive Cues on Surfaces: The Role of Bioactive Ligand Selection
Bibliographic record
Abstract
The development of 2D or 3D bioactive platforms for rapidly isolating pure populations of cells from adult stem cells holds promise for advancing the understanding of cellular mechanisms, drug testing, and tissue engineering. Over the years, methods have emerged to synthesize bioactive micro- and nanostructured 2D materials capable of directing stem cell fate. We introduce a novel method for randomly micro- or nanopatterning any protein/peptide onto both 2D and 3D scaffolds via spray technology. Our goal is to investigate the impact of arranging bioactive micropatterns (ordered vs disordered) on surfaces to guide human mesenchymal stem cell (hMSC) differentiation. The spray technology efficiently coats materials with controlled, cost-effective bioactive micropatterns in various sizes and shapes. BMP-2 mimetic peptides were covalently grafted, individually or in combination with RGD peptides, onto activated polyethylene terephthalate (PET) surfaces through a spraying process, incorporating nano/microscale parameters like size, shape, and composition. The study explores different peptide distributions on surfaces and various peptide combinations. Four surfaces were homogeneously functionalized with these peptides (M1 to M4 with various densities of peptides), and six surfaces with disordered micro- and nanopatterns of peptides (S0 to S5 with different sizes of peptide patterns) were synthesized. Fluorescence microscopy assessed peptide distribution, followed by hMSC culture for 2 weeks, and evaluated osteogenic differentiation via immunocytochemistry and RT-qPCR for osteoblast and osteocyte markers. Cells on uniformly peptide-functionalized surfaces exhibited cuboidal forms, while those on surfaces with disordered patterns tended toward columnar or cuboidal shapes. Surfaces S4 and S5 showed dendrite-like formations resembling an osteocyte morphology. S5 showed significant overexpression of osteoblast (OPN) and osteocyte markers (E11, DMP1, and SOST) compared to control surfaces and other micropatterned surfaces. Notably, despite sharing an equivalent quantity of peptides with a homogeneous functionalized surface, S5 displayed a distinct distribution of peptides, resulting in enhanced osteogenic differentiation of hMSCs.
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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) | 0.000 | 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".