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Record W4408142346 · doi:10.1093/nar/gkaf105

Packaged delivery of CRISPR–Cas9 ribonucleoproteins accelerates genome editing

2025· article· en· W4408142346 on OpenAlexfundno aff
Hannah Karp, Madeline Zoltek, Kevin Wasko, Angel Menendez Vazquez, Jinna Brim, Wayne Ngo, Alanna Schepartz, Jennifer A. Doudna

Bibliographic record

VenueNucleic Acids Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaEmerson CollectiveHoward Hughes Medical InstituteNational Institutes of HealthNational Science Foundation
KeywordsBiologyCRISPRGenome editingRibonucleoproteinCas9GeneticsComputational biologyGuide RNAGenomeGeneRNA

Abstract

fetched live from OpenAlex

Effective genome editing requires a sufficient dose of CRISPR-Cas9 ribonucleoproteins (RNPs) to enter the target cell while minimizing immune responses, off-target editing, and cytotoxicity. Clinical use of Cas9 RNPs currently entails electroporation into cells ex vivo, but no systematic comparison of this method to packaged RNP delivery has been made. Here we compared two delivery strategies, electroporation and enveloped delivery vehicles (EDVs), to investigate the Cas9 dosage requirements for genome editing. Using fluorescence correlation spectroscopy, we determined that >1300 Cas9 RNPs per nucleus are typically required for productive genome editing. EDV-mediated editing was >30-fold more efficient than electroporation, and editing occurs at least 2-fold faster for EDV delivery at comparable total Cas9 RNP doses. We hypothesize that differences in efficacy between these methods result in part from the increased duration of RNP nuclear residence resulting from EDV delivery. Our results directly compare RNP delivery strategies, showing that packaged delivery could dramatically reduce the amount of CRISPR-Cas9 RNPs required for experimental or clinical genome editing.

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)0.001
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 score0.040
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.365
Teacher spread0.340 · 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

Citations21
Published2025
Admission routes1
Has abstractyes

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