Assessment of Global DNA Double-Strand End Resection using BrdU-DNA Labeling coupled with Cell Cycle Discrimination Imaging
Bibliographic record
Abstract
The study of the DNA damage response (DDR) is a complex and essential field, which has only become more important due to the use of DDR-targeting drugs for cancer treatment. These targets are poly(ADP-ribose) polymerases (PARPs), which initiate various forms of DNA repair. Inhibiting these enzymes using PARP inhibitors (PARPi) achieves synthetic lethality by conferring a therapeutic vulnerability in homologous recombination (HR)-deficient cells due to mutations in breast cancer type 1 (BRCA1), BRCA2, or partner and localizer of BRCA2 (PALB2). Cells treated with PARPi accumulate DNA double-strand breaks (DSBs). These breaks are processed by the DNA end resection machinery, leading to the formation of single-stranded (ss) DNA and subsequent DNA repair. In a BRCA1-deficient context, reinvigorating DNA resection through mutations in DNA resection inhibitors, such as 53BP1 and DYNLL1, causes PARPi resistance. Therefore, being able to monitor DNA resection in cellulo is critical for a clearer understanding of the DNA repair pathways and the development of new strategies to overcome PARPi resistance. Immunofluorescence (IF)-based techniques allow for monitoring of global DNA resection after DNA damage. This strategy requires long-pulse genomic DNA labeling with 5-bromo-2'-deoxyuridine (BrdU). Following DNA damage and DNA end resection, the resulting single-stranded DNA is specifically detected by an anti-BrdU antibody under native conditions. Moreover, DNA resection can also be studied using cell cycle markers to differentiate between various phases of the cell cycle. Cells in the S/G2 phase allow the study of end resection within HR, whereas G1 cells can be used to study non-homologous end joining (NHEJ). A detailed protocol for this IF method coupled to cell cycle discrimination is described in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".