Unraveling the assembloid: Real-time monitoring of dopaminergic neurites in an inter-organoid pathway connecting midbrain and striatal regions
Bibliographic record
Abstract
Modern in vitro technologies for preclinical research, including organ-on-a-chip, organoids- and assembloid-based systems, have rapidly emerged as pivotal tools for elucidating disease mechanisms and assessing the efficacy of putative therapeutics. In this context, advanced in vitro models of Parkinson's Disease (PD) offer the potential to accelerate drug discovery by enabling effective platforms that recapitulate both physiological and pathological attributes of the in vivo environment. Although these systems often aim at replicating the PD-associated loss of dopaminergic (DA) neurons, only a few have modelled the degradation of dopaminergic pathways as a way to mimic the disruption of downstream regulation mechanisms that define the characteristic motor symptoms of the disease. To this end, assembloids have been successfully employed to recapitulate neuronal pathways between distinct brain regions. However, the investigation and characterization of these connections through neural tracing and electrophysiological analysis remain a technically challenging and time-consuming process. Here, we present a novel bioengineered platform consisting of surface-grown midbrain and striatal organoids at opposite sides of a self-assembled DA pathway. In particular, dopaminergic neurons and striatal GABAergic neurons spontaneously form DA connections across a microelectrode array (MEA), specifically integrated for the real-time monitoring of electrophysiological development and stimuli response. Calcium imaging data showed spiking synchronicity of the two organoids forming the IOPs demonstrating that they are functionally connected. MEA recordings confirm a more robust response to the DA neurotoxin 6-OHDA compared to midbrain organoids alone, thereby validating the potential of this technology to generate highly tractable, easily extractable real-time functional readouts to investigate the dysfunctional dopaminergic network of PD patients.
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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 | 0.001 |
| Open science | 0.000 | 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 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".