Widespread Translational Remodeling during Human Neuronal Differentiation
Bibliographic record
Abstract
Faithful cellular differentiation requires temporally precise activation of gene expression programs, which are coordinated at the transcriptional and translational levels. Neurons express the most complex set of mRNAs of any human tissue, but translational changes during neuronal differentiation remain incompletely understood. Here, we induced forebrain neuronal differentiation of human embryonic stem cells (hESCs) and measured genome-wide RNA and translation levels with transcript-isoform resolution. We found that thousands of genes change translation status during differentiation without a corresponding change in RNA level. Specifically, we identified mTOR signaling as a key driver for elevated translation of translation-related genes in hESCs. In contrast, translational repression in active neurons is mediated by regulatory sequences in 3' UTRs. Together, our findings identify extensive translational control changes during human neuronal differentiation and a crucial role of 3' UTRs in driving cell-type-specific translation.
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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".