MétaCan
Menu
Back to cohort
Record W2809595124 · doi:10.1089/crispr.2017.0020

TIE: A Method to Electroporate Long DNA Templates into Preimplantation Embryos for CRISPR-Cas9 Gene Editing

2018· article· en· W2809595124 on OpenAlexafffund
Hooman Bagheri, Hana Friedman, Harry Shao, Yumaine Chong, Chiu‐An Lo, Farida Emran, Ibrahim Kays, Xiang-Jiao Yang, Ellis Cooper, Brian E. Chen, Katherine A. Siminovitch, Alan C. Peterson

Bibliographic record

VenueThe CRISPR Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteMcGill University Health CentreMcGill University
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsElectroporationCRISPRGenome editingCas9MicroinjectionPlasmidBiologyEndonucleaseOligonucleotideDNATemplateComputational biologyGeneMolecular biologyCell biologyGeneticsComputer science

Abstract

fetched live from OpenAlex

Precise genome editing using CRISPR typically requires delivery of guide RNAs, Cas9 endonuclease, and DNA repair templates. Both microinjection and electroporation effectively deliver these components into mouse zygotes provided the DNA template is an oligonucleotide of only a few hundred base pairs. However, electroporation completely fails with longer double-stranded DNAs leaving microinjection as the only delivery option. Here, we overcome this limitation by first injecting all CRISPR components, including long plasmid-sized DNA templates, into the sub-zona pellucida space. There they are retained, supporting subsequent electroporation. We show that this simple and well-tolerated method achieves intracellular reagent concentrations sufficient to effect precise gene edits.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.012
GPT teacher head0.368
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations10
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueThe CRISPR JournalSame topicCRISPR and Genetic EngineeringFrench-language works237,207