3D Bioprinting amyloid plaque‐like deposits, a strategy to model Alzheimer's disease <i>in vitro</i>
Bibliographic record
Abstract
BACKGROUND: Amyloid β (Aβ) aggregation is a key neuropathological hallmark of Alzheimer's disease (AD) and is believed to trigger the pathological cascade leading to neurodegeneration. However, despite decades of research, the mechanisms underlying amyloidogenesis remain unclear, partly due to the lack of representative models that accurately recapitulate human pathology. Given that Aβ aggregates form in the extracellular space through the interaction with the extracellular matrix (ECM), conventional 2D cultures are inadequate for studying this process. Conversely, in vivo models provided insights but failed to fully replicate human Aβ aggregation dynamics, neurotoxicity, and disease progression. Recent advances in 3D bioprinting now allow the creation of physiologically relevant human brain models by integrating human induced pluripotent stem cells (hiPSCs) with ECM-like biomaterials (bioinks). METHOD: Here, we developed a 3D bioprinted human brain model for long-term culture of iPSC-derived familial AD (fAD) cortical neurons, astrocytes, and microglia in a multilayer wood-pile structure that mimics the cytoarchitecture of the human cortex. To model Aβ plaque formation, we incorporated synthetic fibrillar Aβ42 (fAβ42) into the bioink, leveraging AβV717I mutant neurons as a substrate while using fAβ42 as a seed to promote aggregation. RESULT: After three weeks of culture, we observed a statistically significant decrease in endogenous Aβ40, Aβ42, and the Aβ40/Aβ42 ratio in the conditioned medium, suggesting increased Aβ aggregation. Immunostaining confirmed increased accumulation of mOC87 and 4G8-positive Aβ deposits within the 3D-printed constructs. CONCLUSION: Our findings demonstrate the feasibility of mimicking amyloidogenesis in a 3D human-derived brain model, overcoming limitations of traditional in vitro models. This approach provides a human-relevant platform to study Aβ nucleation and plaque formation in real-time, offering a novel tool for AD research and drug discovery without relying on animal models.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".