Rapid scaffold-free cell sheet formation and their patterning as building blocks of complex 3D tissue constructs
Bibliographic record
Abstract
, magnetic or electrical fields), which can damage cells, compromise sheet integrity, or demand costly, non-adaptable equipment. Here, we present a rapid, self-assembly-based technique using unmodified polydimethylsiloxane (PDMS) molds as culture vessels. When seeded at a critical cell density, adherent cells spontaneously self-assemble into planar 3D cell sheets within 6 hours, without substrate modification or specialized equipment. Our qRT-PCR analysis revealed significant upregulation of E-cadherin in cell sheets, confirming that cell-cell adhesion, rather than cell-substrate anchorage, drives sheet formation. We showed that our technique is versatile, supporting the creation of large-area and patterned sheets, stacked multi-layer constructs, and co-culture configurations. Notably, fibroblast cell sheets, demonstrated progressive ECM production, with histological analysis confirming collagen deposition over time. Overall, our approach preserves cell viability and function while offering a simple, rapid, and cost-effective alternative to conventional methods for fabricating cell sheets. This platform holds broad potential for applications in tissue engineering, regenerative medicine, disease modeling, and cultivated meat production.
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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 | 0.001 |
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".