Footprintless disruption of prosurvival genes in aneuploid cancer cells using CRISPR/Cas9 technology
Bibliographic record
Abstract
CRISPR/Cas9 has emerged as a powerful methodology for the targeted editing of genomic DNA sequences. Nevertheless, the intrinsic inefficiency of transfection methods required to use this technique with cultured cells requires the selection and isolation of successfully modified cells, which invariably subjects the cells to stress. Here we report a workflow that allows the isolation of genomically modified cells, even where loss of functional alleles constitutes a selective disadvantage owing to impaired ability to survive stress. Using targeted disruption of the Id1 and Id3 genes in murine B16-F10 and Ret melanoma cell lines as an example, we show that the method allows for the footprintless isolation of CRISPR/Cas9-modified aneuploid cancer cells. We also provide evidence that serial CRISPR/Cas9 modifications can occur, for example when initial homologous recombination events introduce cryptic PAM sequences, and demonstrate that multiple alleles can be successfully targeted in aneuploid cancer cells. By sequencing individual alleles we also found evidence for CRISPR/Cas9-induced transposable element insertion, albeit at a low frequency. This workflow should have broad application in the functional analysis of prosurvival gene function in cultured cells.
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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 | 0.001 |
| 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".