Image Correlation Spectroscopy is a Robust Tool to Quantify Cellular DNA Damage Response
Bibliographic record
Abstract
Abstract The DNA Damage response (DDR) is both essential and highly complex. Evaluating the DDR is a critical aspect of cell biology. Counting DNA damage foci is one of the most common approaches to study the DDR. Yet, quantification of protein foci suffers from experimental limitations, subjectivity of analysis and is restricted to a handful of the hundreds of DDR proteins. Here we apply image correlation spectroscopy (ICS) to quantify the local clustering at sites of DNA damage directly. We found that ICS outperformed foci counting of traditional DDR markers and enabled quantification of other markers without the complex labeling procedures that are otherwise required. ICS analysis also provided insight into DDR protein recruitment that was previously undetectable. Further expansion incorporating analysis to cell cycle classification demonstrates a rapid, non-biased approach to fully study the DNA damage response within cells. ICS analysis presents an objective, quantitative image analysis technique to study the DNA damage response in unaltered cells that we expect will significantly enhance quantitative DNA damage response research.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".