Characterization of Stress Granule Protein Turnover in Neuronal Progenitor Cells Using Correlative STED and NanoSIMS Imaging
Bibliographic record
Abstract
Stress granules (SGs) are stress-induced biomolecular condensates which originate primarily from inactivated RNA translation machinery and translation initiation factors. SG formation is an important defensive mechanism for cell survival, while its dysfunction has been linked to neurodegenerative diseases. However, the molecular mechanisms of SG assembly and disassembly, as well as their impacts on cellular recovery, are not fully understood. More thorough investigations into the molecular dynamics of SG pathways are required to understand the pathophysiological roles of SGs in cellular systems. Here, we characterize the SG and cytoplasmic protein turnover in neuronal progenitor cells (NPCs) under stressed and non-stressed conditions using correlative STED and NanoSIMS imaging. We incubate NPCs with isotopically labelled (15N) leucine and stress them with the ER stressor thapsigargin (TG). A correlation of STED and NanoSIMS allows the localization of individual SGs (using STED), and their protein turnover can then be extracted based on the 15N/14N ratio (using NanoSIMS). We found that TG-induced SGs, which are highly dynamic domains, recruit their constituents predominantly from the cytoplasm. Moreover, ER stress impairs the total cellular protein turnover regimen, and this impairment is not restored after the commonly proceeded stress recovery period.
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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) | 0.000 | 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".