Nuclear domain ‘knock-in’ screen for the evaluation and identification of small molecule enhancers of CRISPR-based genome editing
Bibliographic record
Abstract
CRISPR is a genome-editing platform that makes use of the bacterially-derived endonuclease Cas9 to introduce DNA double-strand breaks at precise locations in the genome using complementary guide RNAs. We developed a nuclear domain knock-in screen, whereby the insertion of a gene encoding the green fluorescent protein variant Clover is inserted by Cas9-mediated homology directed repair (HDR) within the first exon of genes that are required for the structural integrity of subnuclear domains such as the nuclear lamina and promyelocytic leukemia nuclear bodies (PML NBs). Using this approach, we compared strategies for enhancing CRISPR-mediated HDR, focusing on known genes and small molecules that impact non-homologous end joining (NHEJ) and homologous recombination (HR). Ultimately, we identified the small molecule RS-1 as a potent enhancer of CRISPR-based genome editing, enhancing HDR 3- to 6-fold depending on the locus and transfection method. We also characterized U2OS human osteosarcoma cells expressing Clover-tagged PML and demonstrate that this strategy generates cell lines with PML NBs that are structurally and functionally similar to bodies in the parental cell line. Thus, the nuclear domain knock-in screen that we describe provides a simple means of rapidly evaluating methods and small molecules that have the potential to enhance Cas9-mediated HDR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".