<i>Peel-1</i> negative selection promotes screening-free CRISPR-Cas9 genome editing in <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
Abstract Improved genome engineering methods that enable automation of large and precise edits are essential for systematic investigations of genome function. We adapted peel-1 negative selection to an optimized Dual-Marker Selection (DMS) cassette protocol for CRISPR-Cas9 genome engineering in Caenorhabditis elegans and observed robust increases in multiple measures of efficiency that were consistent across injectors and four genomic loci. The use of Peel-1-DMS selection killed animals harboring transgenes as extrachromosomal arrays and spared genome edited integrants, often circumventing the need for visual screening to identify genome edited animals. To demonstrate the applicability of the approach, we created deletion alleles in the putative proteasomal subunit pbs-1 and the uncharacterized gene K04F10.3 and used machine vision to automatically characterize their phenotypic profiles, revealing homozygous essential and heterozygous behavioral phenotypes. These results provide a robust and scalable approach to rapidly generate and phenotype genome edited animals without the need for screening or scoring by eye. Author summary The ability to directly manipulate the genome and observe the resulting effects on the traits of an organism is a powerful approach to investigate gene function. CRISPR-based approaches to genome engineering have revolutionized such functional studies across model organisms but still face major challenges that limit the scope and complexity of projects that can be achieved in practice. Automating genome engineering and phenotyping would enable large-scale investigations of genome function in animals. Here, we describe the adaptation of peel-1 negative selection to an optimized dual-marker selection cassette CRISPR-Cas9 genome engineering method in C. elegans and combine it with automated machine vision phenotyping to achieve functional studies without the need for screening or scoring by eye. To demonstrate the applicability of the approach, we generated novel deletion alleles in two understudied genes, pbs-1 and K04F10.3, and used machine vision to characterize their phenotypic profiles, revealing homozygous lethal and heterozygous behavioral phenotypes. Our results open the door to systematic investigations of genome function in this model organism.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".