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Record W3097886583 · doi:10.3791/61693

In Vivo CRISPR/Cas9 Screening to Simultaneously Evaluate Gene Function in Mouse Skin and Oral Cavity

2020· article· en· W3097886583 on OpenAlexafffund
Sampath K. Loganathan, Ahmad Malik, Ellen Langille, Chen Luxenburg, Daniel Schramek

Bibliographic record

VenueJournal of Visualized Experiments · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research Institute
FundersCanadian Institutes of Health Research
KeywordsCRISPRGenome editingCas9BiologyComputational biologyGeneFunction (biology)Gene targetingTrans-activating crRNAGenetics

Abstract

fetched live from OpenAlex

Genetically modified mouse models (GEMM) have been instrumental in assessing gene function, modeling human diseases, and serving as preclinical model to assess therapeutic avenues. However, their time-, labor- and cost-intensive nature limits their utility for systematic analysis of gene function. Recent advances in genome-editing technologies overcome those limitations and allow for the rapid generation of specific gene perturbations directly within specific mouse organs in a multiplexed and rapid manner. Here, we describe a CRISPR/Cas9-based method (Clustered Regularly Interspaced Short Palindromic Repeats) to generate thousands of gene knock-out clones within the epithelium of the skin and oral cavity of mice, and provide a protocol detailing the steps necessary to perform a direct in vivo CRISPR screen for tumor suppressor genes. This approach can be applied to other organs or other CRISPR/Cas9 technologies such as CRISPR-activation or CRISPR-inactivation to study the biological function of genes during tissue homeostasis or in various disease settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.421
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2020
Admission routes2
Has abstractyes

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