Impact of biophysical cues on brain organoid growth and development
Bibliographic record
Abstract
Accumulating evidence illustrates the importance of biophysical cues such as mechanical properties and stresses in embryonic development of organs, as well as disease progression.Researchers in the tissue engineering field are increasingly calling for the incorporation of these cues into strategies for biophysical guidance of engineered tissue development, both for generating improved experimental models as well as generating tissue for regenerative medicine purposes.However, studies of the influence of such biophysical cues on brain organoids are limited.Brain organoids are self-organizing, 3D tissue-engineered models of the human brain that are grown from stem cells and mimic certain aspects of embryonic brain development.These important models are the current state-of-the-art in neuroscience, enabling studies in human models that are impossible with other systems.This thesis explores the effects of several biophysical cues on brain organoid growth and development, namely matrix stiffness and geometry, overall and at the organoid periphery.First, the material properties of the hydrogel used to encapsulate brain organoids were modified to change the physical nature of the organoid microenvironment.Stiffer hydrogels yielded smaller midbrain organoids with increased neuronal maturation, and altered internal microarchitectures; specifically, characteristic developmental structures known as neural rosettes were smaller and fewer.Next, to manipulate overall geometry of pre-formed organoids, a compressive hydrogel molding platform was developed.Using this platform, breast cancer spheroids were molded into simple shapes, and brain organoids were molded into rings, by shaping the organoid around a post to fuse with itself.Tissue markers suggested these ring-shaped organoids differentiated as expected within these devices, indicating the platform could be used to mold tissue building blocks into a variety of shapes.Lastly, a system was engineered to create assembloids from brain organoids with passively shaped peripheries, and investigate the influence of the peripheral geometry.The results suggested that axonal projections from midbrain organoids exhibited target-seeking behaviour, and that geometry influences cell migration out of cerebral organoids.The platform could be used to observe such cellular behaviours in culture models of neural circuit formation.Overall, these new tools and insights could lead to integration of biophysical cues into brain
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".