CRISPR-Click Enables Multi-Gene Editing with Modular Synthetic sgRNAs
Bibliographic record
Abstract
Gene editing systems such as CRISPR/Cas9 readily enable individual gene phenotypes to be studied through loss-of-function. However, in certain instances, gene compensation can obfuscate the results of these studies, necessitating the editing of multiple genes to properly identify biological pathways and protein function. Performing multiple genetic modifications in cells remains difficult due to the requirement for multiple rounds of gene editing. While fluorescently labeled guide RNAs (gRNAs) are routinely used in laboratories for targeting CRISPR/Cas9 to disrupt individual loci, technical limitations in single guide RNA (sgRNA) synthesis hinder the expansion of this approach to multi-color cell sorting. Here, we describe a modular strategy for synthesizing sgRNAs where each target sequence is conjugated to a unique fluorescent label, which enables fluorescence-assisted cell sorting (FACS) to isolate cells that incorporate the desired combination of gene-editing constructs. We demonstrate that three short strands of RNA functionalized with strategically placed 3’-azide and 5’-alkyne terminal deoxyribonucleotides can be assembled in a one-step, template-assisted, copper-catalyzed alkyne-azide cycloaddition (CuAAC) to generate fully functional, fluorophore-modified sgRNAs. Using these synthetic sgRNA in combination with FACS, we achieved selective cleavage of two targeted genes, either separately as a single color experiment or in combination as a dual-color experiment. These data indicate that our strategy for generating doubly-clicked sgRNA allows for Cas9 activity in cells. By minimizing the size of each RNA fragment to 41 nucleotides or less, this strategy is well suited for custom, scalable synthesis of sgRNAs.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".