Differentiation Defect Into <scp>GABAergic</scp> Neurons in Cerebral Organoids From Autism Patients
Bibliographic record
Abstract
OBJECTIVES: Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects social communication and behaviors. While previous studies using animal models have substantially expanded our knowledge about ASD, the lack of an appropriate human model system that accurately recapitulates the human-specific pathophysiology of ASD hinders the precise understanding of its etiology and the development of effective therapies. This study aims to replicate pathological phenotypes in cerebral organoids derived from idiopathic ASD patients and to conduct proof-of-concept research for the development of ASD therapeutics. METHODS: We conducted an in vitro disease modeling study using cerebral organoids derived from three idiopathic ASD patients. Additionally, we performed organoid-based phenotypic drug screening to identify potential therapeutic compounds that could ameliorate the phenotypes observed in cerebral organoids derived from idiopathic ASD patients. RESULTS: Here we show that cerebral organoids derived from idiopathic ASD patients display malformation of the ventricular zones and impaired early neuronal differentiation. Through organoid-based phenotypic drug screening, we successfully generated cerebral organoids with normal tissue architecture in which the delayed neuronal differentiation could also be accelerated. Notably, cerebral organoids from ASD patients exhibited a reduced number of GABAergic neurons compared to healthy controls, resulting in an imbalance in the excitatory and inhibitory neuron ratio. The differentiation defects into GABAergic neurons in patient-derived cerebral organoids could be rescued by treating with either IGF1 or Gabapentin, a GABA agonist. CONCLUSIONS: Our findings provide a framework for utilizing patient-derived cerebral organoids in the development of personalized pharmaceutical treatment for ASD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".