Quantitative assaying of SpCas9-NG with fluorescent reporters
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".