Optimizing Stem Cell Expansion: The Role of Substrate Stiffness in Enhancing Dental Pulp Stem Cell Quiescence and Regeneration
Bibliographic record
Abstract
INTRODUCTION: Quiescent stem cells exhibit unique self-renewal and engraftment abilities vital for regenerative therapies, but these diminish during ex vivo culture. This study investigates how substrate stiffness regulates the balance between dental pulp stem cell (DPSC) quiescence, activation, and senescence and explores the role of extracellular matrix stiffness in modulating DPSC fate via the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway. METHODS: Polydimethylsiloxane substrates with varying stiffness in 2D (2 kPa, 50 kPa) and 3D (50 kPa) were fabricated. Mechanical properties and porosity were characterized. Human DPSCs were cultured for 7 and 14 days. Senescence was assessed by senescence β-galactosidase activity, nuclear changes by immunofluorescence staining, and gene expression of quiescence, self-renewal, and senescence markers by reverse transcription quantitative polymerase chain reaction. NF-κB activation was analyzed through p65 nuclear translocation. Statistical analysis employed one-way analysis of variance with post-Tukey tests (P < .05). RESULTS: The porous (310 ± 63 μm) 3D substrate had 50 kPa stiffness. DPSCs on 50 kPa substrates exhibited increased nuclear size and senescence in both 2D and 3D contexts. Softer 2 kPa substrates promoted quiescence, evidenced by reduced chromatin condensation and senescence, alongside upregulation of quiescence associated genes (BMI-1) and pluripotency markers (NANOG, OCT4, SOX2). NF-κB activation was observed on soft substrates, marked by nuclear translocation of p65 and upregulated NF-κB pathway genes, correlating with enhanced stemness and reduced senescence. CONCLUSIONS: This study highlights the pivotal role of substrate stiffness in modulating stem cell fate. Softer substrates preserve DPSC quiescence, reduce senescence, and enhance stemness through NF-κB pathway activation, offering insights into optimizing ex vivo DPSC expansion for therapeutic applications.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".