Investigation of Protein Recruitment to DNA Lesions Using 405 Nm Laser Micro-irradiation
Bibliographic record
Abstract
The DNA Damage Response (DDR) uses a plethora of proteins to detect, signal, and repair DNA lesions. Delineating this response is critical to understand genome maintenance mechanisms. Since recruitment and exchange of proteins at lesions are highly dynamic, their study requires the ability to generate DNA damage in a rapid and spatially-delimited manner. Here, we describe procedures to locally induce DNA damage in human cells using a commonly available laser-scanning confocal microscope equipped with a 405 nm laser line. Accumulation of genome maintenance factors at laser stripes can be assessed by immunofluorescence (IF) or in real-time using proteins tagged with fluorescent reporters. Using phosphorylated histone H2A.X (γ-H2A.X) and Replication Protein A (RPA) as markers, the method provides sufficient resolution to discriminate locally-recruited factors from those that spread on adjacent chromatin. We further provide ImageJ-based scripts to efficiently monitor the kinetics of protein relocalization at DNA damage sites. These refinements greatly simplify the study of the DDR dynamics.
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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) | 0.002 | 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".