Dynamic Bioreactors with Integrated Microfabricated Devices for Mechanobiological Screening
Bibliographic record
Abstract
Biomechanical stimulation is a common strategy to improve the growth, maturation, and function of a variety of engineered tissues. However, identifying optimized biomechanical conditioning protocols is challenging, as cell responses to mechanical stimuli are modulated by other multifactorial microenvironmental cues, including soluble factors and biomaterial properties. Traditional bioreactors lack the throughput necessary for combinatorial testing of cell activity in mechanically stimulated engineered tissues. Microfabricated systems can improve experimental throughput, but often do not provide uniform mechanical loading, are challenging to use, lack robustness, and offer limited amounts of cells and tissue for analysis. To address the need for higher-throughput, combinatorial testing of cell activity in a tissue engineering context, we developed a hybrid approach, in which flexible polydimethylsiloxane microfabricated inserts were designed to simultaneously generate multiple tensile strains when stretched cyclically in a standard dynamic bioreactor. In the embodiment presented in this study, each insert contained an array of 35 dog bone-shaped wells in which cell-seeded microscale hydrogels can be polymerized, with up to eight inserts stretched simultaneously in the bioreactor. Uniformity of the applied strains, both along the length of a microtissue and across multiple microtissues at the same strain level, was confirmed experimentally. In proof-of-principle experiments, the combinatorial effects of dynamic strain, biomaterial stiffness, and transforming growth factor (TGF)-β1 stimulation on myofibroblast differentiation were tested, revealing both known and novel interaction effects and suggesting tissue engineering strategies to regulate myofibroblast activation. This platform is expected to have wide applicability in systematically probing combinations of mechanobiological tissue engineering parameters for desired effects on cell fate and tissue function. Impact Statement In this study, we introduce a dynamic bioreactor system incorporating microfabricated inserts to enable systematic probing of the effects of combinations of mechanobiological parameters on engineered tissues. This novel platform offers the ease of use, robustness, and well-defined mechanical strain stimuli inherent in traditional dynamic bioreactors, but significantly improves throughput (up to 280 microtissues can be tested simultaneously in the embodiment presented in this study). This platform has wide applicability to systematically probe combinations of dynamic mechanical strain, biomaterial properties, biochemical stimulation, and other parameters for desired effects on cell fate and engineered tissue development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".