Monitoring translation in synaptic fractions using a ribosome profiling strategy
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to develop a method to study genome-wide local translation in biochemically isolated synaptic fractions (synaptoneurosomes). This methodology is of particular interest for neurons, due to the cardinal role of local translational control in neuronal sub-compartments, such as dendrites, for plasticity, learning, memory, and for disorders of the nervous system. NEW METHOD: We combined established methods for purifying synaptoneurosomes with translational profiling (ribosome profiling), a method that employs unbiased next generation sequencing to simultaneously assess transcription and translation in a single sample. RESULTS: The two existing methods are compatible to use in combination and yield high quality sequencing data, which are specific to synaptic compartments. This new protocol provides an easy to implement workflow, which combines biochemical isolation of synaptoneurosomes of varying levels of purity (crude or Percoll gradient purified) with the use of a commercial kit to generate sequencing libraries. COMPARISON WITH EXISTING METHODS: Compared to previous studies of the synaptic translatome, our method shows less contamination with non-neuronal cell types or non-synaptic compartments, increasing the specificity of the data obtained. CONCLUSIONS: Combining the isolation of functional synaptic units with ribosome profiling offers a powerful tool to study local translation in synaptic compartments both in health and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".