Phenotypic Heterogeneity in the DNA Replication Stress Response Revealed by Quantitative Protein Dynamics Measurements
Bibliographic record
Abstract
Abstract Cells respond to environmental stressors by activating programs that result in protein abundance and localization changes. The DNA damage and DNA replication stress responses have been heavily studied and provide exemplars of the roles of protein localization and abundance regulation in proper cellular stress response. While vast amounts of data have been collected to describe the dynamics of yeast proteins in response to numerous external stresses, few have assessed and compared both protein localization kinetics and phenotypic heterogeneity in the same context, particularly during DNA replication stress. We developed a robust yet simple quantification scheme to identify and measure protein localization change events (re-localization) and applied it to the 314 yeast proteins whose subcellular distribution changes following DNA replication stress. We captured different kinetics of protein re-localization, identified proteins with localization changes that were not detected in previous analyses, and defined the extent of heterogeneity in stress-induced protein re-localization. Our imaging platforms and analysis pipeline enables efficient measurements of protein localization phenotypes for single cells over time and will guide future work in elucidating the biological parameters that govern cellular heterogeneity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".