3D bioprinting patient-derived induced pluripotent stem cell models of Alzheimer’s disease using a smart bioink
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD), a progressive neurodegenerative disorder, is becoming increasingly prevalent as our population ages. It is characterized by the buildup of amyloid beta plaques and neurofibrillary tangles containing hyperphosphorylated-tau. The current treatments for AD do not prevent the long-term progression of the disease and pre-clinical models often do not accurately represent its complexity. Bioprinting combines cells and biomaterials to create 3D structures that replicate the native tissue environment and can be used as a tool in disease modeling or drug screening. Methods This work differentiated both healthy and diseased patient–derived human induced pluripotent stems cells (hiPSCs) into neural progenitor cells (NPCs) that were bioprinted using the Aspect RX1 microfluidic printer into dome-shaped constructs. The combination of cells, bioink, and puromorphamine (puro)-releasing microspheres were used to mimic the in vivo environment and direct the differentiation of the NPCs into basal forebrain-resembling cholinergic neurons (BFCN). These tissue models were then characterized for cell viability, immunocytochemistry, and electrophysiology to evaluate their functionality and physiology for use as disease-specific neural models. Results Tissue models were successfully bioprinted and the cells were viable for analysis after 30- and 45-day cultures. The neuronal and cholinergic markers β-tubulin III (Tuj1), forkhead box G1 (FOXG1), and choline acetyltransferase (ChAT) were identified as well as the AD markers amyloid beta and tau. Further, immature electrical activity was observed when the cells were excited with potassium chloride and acetylcholine. Conclusions This work shows the successful development of bioprinted tissue models incorporating patient derived hiPSCs. Such models can potentially be used as a tool to screen promising drug candidates for treating AD. Further, this model could be used to increase the understanding of AD progression. The use of patient derived cells also shows the potential of this model for use in personalized medicine applications.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".