Low-intensity focused ultrasound enables temporal modulation of human midbrain organoid differentiation
Bibliographic record
Abstract
Abstract Controlling the precise timing of biosignaling cues in complex 3D models such as organoids is critical for guiding cellular differentiation and functional maturation. Ultrasound stimulation, a next-generation neuromodulation modality, offers unique advantages due to its target specificity, ability to elicit mechanosensitive responses, and high spatial resolution. However, there is still a lack of precision stimulation platforms and comprehensive biomarker assays for selective control of organoid development. Here, we introduce a modular piezoelectric ultrasound stimulation platform that integrates seamlessly with conventional multi-well plates, enabling selective neuromodulation of midbrain organoids (mBOs). We demonstrate that ultrasound can specifically modulate cellular differentiation by promoting dopaminergic progenitor markers while delaying terminal differentiation. Importantly, ultrasound stimulation did not induce cellular damage, as confirmed by the absence of apoptosis and DNA damage markers. This work demonstrates the potential of focused ultrasound as a safe, non-invasive, and tunable biophysical cue for temporal regulation of organoid differentiation and maturation. Significance Statement Organoids are three dimensional in vitro models derived from human tissue that recapitulate the complex features of organs. However, precise modulation of organoid differentiation relies on biochemical factors, which are limited in their temporal controllability. In this study, we demonstrate that low-intensity ultrasound stimulation enables temporal modulation of midbrain organoid differentiation using a modular multi-well stimulation platform. In particular, we show that ultrasound promotes the proliferation of dopaminergic progenitor cells. These findings suggest that ultrasound can serve as a supplementary mechanical cue to regulate midbrain organoid 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 | 0.000 | 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 | 0.000 |
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".