Disrupting Protein Expression with Double-Clicked sgRNA–Cas9 Complexes: A Modular Approach to CRISPR Gene Editing
Bibliographic record
Abstract
CRISPR-Cas9 is currently the most versatile technique to perform gene editing in living organisms. In this approach, the Cas9 endonuclease is guided toward its DNA target sequence by the guide RNA (gRNA). Chemical synthesis of a functional single gRNA (sgRNA) is nontrivial because of the length of the RNA strand. Recently we demonstrated that a sgRNA can be stitched together from three smaller fragments through a copper-catalyzed azide–alkyne cycloaddition, making the process highly modular. Here we further advance this approach by leveraging this modulator platform by incorporating chemically modified nucleotides at both ends of the modular sgRNA to increase resistance against ribonucleases. Modified nucleotides consisted of a 2′- O -Me group and a phosphorothioate backbone in varying number at both the 5′- and 3′-ends of the sgRNA. It was observed that three modified nucleotides at both ends of the sgRNA significantly increased the success of Cas9 in knocking out a gene of interest. Using these chemically stabilized sgRNAs facilitates multigene editing at the protein level, as demonstrated by successful knockout of both Siglec-3 and Siglec-7 using two fluorophores in conjunction with fluorescence-activated cell sorting. These results demonstrate the versatility of this modular platform for assembling sgRNAs from small, chemically modified strands to simultaneously disrupt the gene expression of two proteins.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".