Microfabricated dynamic brain organoid cocultures to assess the effects of surface geometry on assembloid formation
Bibliographic record
Abstract
Organoids have emerged as valuable tools for the study of development and disease. Assembloids are formed by integrating multiple organoid types to create more complex models. However, the process by which organoids integrate to form assembloids remains unclear and may play an important role in the resulting organoid structure. Here, a microfluidic platform is developed that allows separate culture of distinct organoid types and provides the capacity to partially control the geometry of the resulting organoid surfaces. Removal of a microfabricated barrier then allows the shaped and positioned organoids to interact and form an assembloid. When midbrain and unguided brain organoids were allowed to assemble with a defined spacing between them, axonal projections from midbrain organoids and cell migration out of unguided organoids were observed and quantitatively measured as the two types of organoids fused together. Axonal projection directions were statistically biased toward other midbrain organoids, and unguided organoid surface geometry was found to affect cell invasion. This platform provides a tool to observe cellular interactions between organoid surfaces that are spaced apart in a controlled manner, and may ultimately have value in exploring neuronal migration, axon targeting, and assembloid formation mechanisms.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".