Generating CRISPR/Cas9 Mediated Monoallelic Deletions to Study Enhancer Function in Mouse Embryonic Stem Cells
Bibliographic record
Abstract
Enhancers control cell identity by regulating tissue-specific gene expression in a position and orientation independent manner. These enhancers are often located distally from the regulated gene in intergenic regions or even within the body of another gene. The position independent nature of enhancer activity makes it difficult to match enhancers with the genes they regulate. Deletion of an enhancer region provides direct evidence for enhancer activity and is the gold standard to reveal an enhancer's role in endogenous gene transcription. Conventional homologous recombination based deletion methods have been surpassed by recent advances in genome editing technology which enable rapid and precisely located changes to the genomes of numerous model organisms. CRISPR/Cas9 mediated genome editing can be used to manipulate the genome in many cell types and organisms rapidly and cost effectively, due to the ease with which Cas9 can be targeted to the genome by a guide RNA from a bespoke expression plasmid. Homozygous deletion of essential gene regulatory elements might lead to lethality or alter cellular phenotype whereas monoallelic deletion of transcriptional enhancers allows for the study of cis-regulation of gene expression without this confounding issue. Presented here is a protocol for CRISPR/Cas9 mediated deletion in F1 mouse embryonic stem (ES) cells (Mus musculus(129) x Mus castaneus). Monoallelic deletion, screening and expression analysis is facilitated by single nucleotide polymorphisms (SNP) between the two alleles which occur on average every 125 bp in these 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
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".