Multiplexed pancreatic genome engineering and cancer induction by transfection-based CRISPR/Cas9 delivery in mice
Bibliographic record
Abstract
Mouse transgenesis has provided fundamental insights into pancreatic cancer, but is limited by the long duration of allele/model generation. Here we show transfection-based multiplexed delivery of CRISPR/Cas9 to the pancreas of adult mice, allowing simultaneous editing of multiple gene sets in individual cells. We use the method to induce pancreatic cancer and exploit CRISPR/Cas9 mutational signatures for phylogenetic tracking of metastatic disease. Our results demonstrate that CRISPR/Cas9-multiplexing enables key applications, such as combinatorial gene-network analysis, in vivo synthetic lethality screening and chromosome engineering. Negative-selection screening in the pancreas using multiplexed-CRISPR/Cas9 confirms the vulnerability of pancreatic cells to Brca2-inactivation in a Kras-mutant context. We also demonstrate modelling of chromosomal deletions and targeted somatic engineering of inter-chromosomal translocations, offering multifaceted opportunities to study complex structural variation, a hallmark of pancreatic cancer. The low-frequency mosaic pattern of transfection-based CRISPR/Cas9 delivery faithfully recapitulates the stochastic nature of human tumorigenesis, supporting wide applicability for biological/preclinical 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".