Toward Spatial Identities in Human Brain Organoids-on-Chip Induced by Morphogen-Soaked Beads
Bibliographic record
Abstract
Recent advances in stem-cell technologies include the differentiation of human embryonic stem cells (hESCs) into organ-like structures (organoids). These organoids exhibit remarkable self-organization that resembles key aspects of in vivo organ development. However, organoids have an unpredictable anatomy, and poorly reflect the topography of the dorsoventral, mediolateral, and anteroposterior axes. In vivo the temporal and the spatial patterning of the developing tissue is orchestrated by signaling molecules called morphogens. Here, we used morphogen-soaked beads to influence the spatial identities within hESC-derived brain organoids. The morphogen- and synthetic molecules-soaked beads were interpreted as local organizers, and key transcription factor expression levels within the organoids were affected as a function of the distance from the bead. We used an on-chip imaging device that we have developed, that allows live imaging of the developing hESC-derived organoids. This platform enabled studying the effect of changes in WNT/BMP gradients on the expression of key landmark genes in the on-chip human brain organoids. Titration of CHIR99201 (WNT agonist) and BMP4 directed the expression of telencephalon and medial pallium genes; dorsal and ventral midbrain markers; and isthmus-related genes. Overall, our protocol provides an opportunity to study phenotypes of altered regional specification and defected connectivity, which are found in neurodevelopmental diseases.
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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 | 0.001 |
| 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".