Using Human iPSC-Derived Neurons to Model TAU Aggregation
Bibliographic record
Abstract
Alzheimer's disease and frontotemporal dementia are amongst the most common forms of dementia characterized by the formation and deposition of abnormal TAU in the brain. In order to develop a translational human TAU aggregation model suitable for screening, we transduced TAU harboring the pro-aggregating P301L mutation into control hiPSC-derived neural progenitor cells followed by differentiation into cortical neurons. TAU aggregation and phosphorylation was quantified using AlphaLISA technology. Although no spontaneous aggregation was observed upon expressing TAU-P301L in neurons, seeding with preformed aggregates consisting of the TAU-microtubule binding repeat domain triggered robust TAU aggregation and hyperphosphorylation already after 2 weeks, without affecting general cell health. To validate our model, activity of two autophagy inducers was tested. Both rapamycin and trehalose significantly reduced TAU aggregation levels suggesting that iPSC-derived neurons allow for the generation of a biologically relevant human Tauopathy model, highly suitable to screen for compounds that modulate TAU aggregation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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) | 0.000 | 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".