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Record W4289837269 · doi:10.1101/2022.08.04.502727

Quantitative assaying of SpCas9-NG with fluorescent reporters

2022· preprint· en· W4289837269 on OpenAlexaff
Alexandre Baccouche, Kévin Montagne, Nozomu Yachie, Teruo Fujii, Anthony J. Genot

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of British Columbia
FundersJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyAgence Nationale de la Recherche
KeywordsCas9CRISPREndonucleaseComputational biologyFluorescenceBiologyGenome editingChemistryMolecular biologyEnzymeBiochemistryGene

Abstract

fetched live from OpenAlex

ABSTRACT The Cas9 enzyme has revolutionized biology in less than a decade. Engineering Cas9 to expand its functionality has become a major research goal, yet assaying variants of Cas9 remains a laborious task that is commonly performed with gel electrophoresis. Fluorescence assays have been reported for Cas9 but their utility for assaying variants of Cas9 has not been investigated in detail. Here we use a simple fluorescent assay to resolve differences of activity between the wild type Streptococcus pyogenes Cas9 (SpCas9) and SpCas9-NG, a variant with an expanded PAM repertoire. We compare the kinetics of the two enzymes on dozens of mutated RNA guides – highlighting the benefits of fluorescence such as quantitativity, sensitivity, multiplexing, non-invasiveness and real-timeness. This validates fluorescence as a tool for engineering Cas9 and lays the groundwork for directly evolving Cas9 in microfluidic compartments.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.012
GPT teacher head0.263
Teacher spread0.251 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicCRISPR and Genetic Engineering→French-language works237,207→