Role of Cell Culture Scaffold Stiffness on Sonoporation Efficiency
Bibliographic record
Abstract
OBJECTIVE: Sonoporation employs ultrasound-driven microbubble oscillations to permeabilize cell membranes, offering the potential for intracellular drug delivery. However, its clinical adaption remains limited, primarily due to an incomplete understanding of the mechanisms through which oscillating bubbles disrupt cell membranes. Most mechanistic sonoporation studies have focused on cell monolayers cultured on rigid plastic scaffolds. METHODS: Here we investigated the influence of scaffold stiffness on sonoporation outcome using simultaneous ultra-high-speed imaging (10 million frames/s) to capture bubble dynamics and high-resolution confocal microscopy to assess cell membrane response and model drug uptake. Monodisperse 2.3 μm radius microbubbles were used to sonoporate single human umbilical vein endothelial cells cultured on either a soft hydrogel scaffold or a rigid polymer membrane. Ultrasound driving frequency and acoustic pressure amplitude were varied, while the pulse length was fixed at 15 cycles. RESULTS: Our results show that the slope of sonoporation efficiency versus microbubble radial excursion curve decreased by a factor of 30 when using the soft scaffold versus the rigid one, despite no apparent differences in microbubble dynamics. Furthermore, the nearly identical sonoporation efficiency versus radial excursion curves across the employed ultrasound frequencies of 0.5, 1.0 and 2.0 MHz suggest that acoustic radiation forces and normal or shear stresses are unlikely to be the primary mechanisms driving the observed differences. Additionally, membrane pore size increased with frequency for the rigid scaffold but decreased for the soft scaffold. CONCLUSION: Our findings highlight the critical role of mechanical scaffold properties in determining sonoporation outcomes, where softer scaffolds result in reduced membrane disruption and altered pore formation dynamics despite unchanged bubble oscillation behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".