REST suppression mediates neural conversion of adult human fibroblasts via microRNA‐dependent and ‐independent pathways
Bibliographic record
Abstract
Direct conversion of human fibroblasts into mature and functional neurons, termed induced neurons (iNs), was achieved for the first time 6 years ago. This technology offers a promising shortcut for obtaining patient- and disease-specific neurons for disease modeling, drug screening, and other biomedical applications. However, fibroblasts from adult donors do not reprogram as easily as fetal donors, and no current reprogramming approach is sufficiently efficient to allow the use of this technology using patient-derived material for large-scale applications. Here, we investigate the difference in reprogramming requirements between fetal and adult human fibroblasts and identify REST as a major reprogramming barrier in adult fibroblasts. Via functional experiments where we overexpress and knockdown the REST-controlled neuron-specific microRNAs miR-9 and miR-124, we show that the effect of REST inhibition is only partially mediated via microRNA up-regulation. Transcriptional analysis confirmed that REST knockdown activates an overlapping subset of neuronal genes as microRNA overexpression and also a distinct set of neuronal genes that are not activated via microRNA overexpression. Based on this, we developed an optimized one-step method to efficiently reprogram dermal fibroblasts from elderly individuals using a single-vector system and demonstrate that it is possible to obtain iNs of high yield and purity from aged individuals with a range of familial and sporadic neurodegenerative disorders including Parkinson's, Huntington's, as well as Alzheimer's disease.
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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".