Harnessing human iPSC-microglia for CNS-wide delivery of disease-modifying proteins
Bibliographic record
Abstract
Widespread delivery of therapeutic proteins to the brain remains challenging. To determine whether human induced pluripotent stem cell (iPSC)-microglia (iMG) could enable brain-wide and pathology-responsive delivery of therapeutic cargo, we utilized CRISPR gene editing to engineer iMG to express the Aβ-degrading enzyme neprilysin under control of the plaque-responsive promoter, CD9. To further determine whether increased engraftment enhances efficacy, we utilized a CSF1R-inhibitor resistance approach. Interestingly, both localized and brain-wide engraftment in Alzheimer's disease (AD) mice reduced multiple biochemical measures of pathology. However, within the plaque-dense subiculum, reductions in plaque load, dystrophic neurites, and astrogliosis and preservation of neuronal density were only achieved following widespread microglial engraftment. Lastly, we examined chimeric models of breast cancer brain metastases and demyelination, demonstrating that iMG adopt diverse transcriptional responses to differing neuropathologies, which could be harnessed to enable widespread and pathology-responsive delivery of therapeutics to the CNS.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".