MicroRNA-Based Promotion of Human Neuronal Differentiation and Subtype Specification
Bibliographic record
Abstract
MicroRNAs are key regulators of neural cell proliferation, differentiation and fate choice. Due to the limited access to human primary neural tissue, the role of microRNAs in human neuronal differentiation remains largely unknown. Here, we use a population of long-term self-renewing neuroepithelial-like stem cells (lt-NES cells) derived from human embryonic stem cells to study the expression and function of microRNAs at early stages of human neural stem cell differentiation and neuronal lineage decision. Based on microRNA expression profiling followed by gain- and loss-of-function analyses in lt-NES cells and their neuronal progeny, we demonstrate that miR-153, miR-324-5p/3p and miR-181a/a contribute to the shift of lt-NES cells from self-renewal to neuronal differentiation. We further show that miR-125b and miR-181a specifically promote the generation of neurons of dopaminergic fate, whereas miR-181a inhibits the development of this neurotransmitter subtype. Our data demonstrate that time-controlled modulation of specific microRNA activities not only regulates human neural stem cell self-renewal and differentiation but also contributes to the development of defined neuronal subtypes.
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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".