Automated Detection and Quantification of Granular Cell Compartments
Bibliographic record
Abstract
Many cellular processes are organized in a compartmentalized and dynamic fashion to ensure effective adaptation to physiological changes. Thus, in response to stress and disease, cells initiate protective mechanisms to restore homeostasis. Among these mechanisms are the arrest of translation and remodeling of ribonucleoprotein complexes into granular compartments in the cytoplasm, known as stress granules (SGs). To date, the analysis of SGs has relied on the manual demarcation and measurement of the compartment, making quantitative studies time-consuming, while preventing the efficient use of high-throughput technology. We developed the first fully automated, computer-based procedures that measure the association of fluorescent molecules with granular compartments. Our methods quantify automatically multiple granule parameters and generate data at the level of single cells or individual SGs. These techniques detect simultaneously in an automated fashion proteins and RNAs located in SGs. The effectiveness of our protocols is demonstrated by studies that reveal several of the unique biological and structural characteristics of SGs. In particular, we show that the type of stress determines granule size and composition, as illustrated by the concentration of poly(A)-RNA and a specific SG marker protein. Furthermore, we took advantage of the computer-based and automated methods to design assays suitable for high-throughput screening.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".