RNA-Activated CRISPR/Cas12a Nanorobots Operating in Living Cells
Bibliographic record
Abstract
Active clustered regularly interspaced short palindromic repeats (CRISPR/Cas12a) systems possess both cis -cleavage (targeted) and trans -cleavage (collateral) activities, which are useful for genome engineering and diagnostic applications. Both single- and double-stranded DNA can activate crRNA–Cas12a ribonucleoprotein (RNP) to achieve cis - and trans -cleavage enzymatic activities. However, it is not clear whether RNA can activate the CRISPR/Cas12a system and what is critical to the trans -cleavage activity. We report here that RNA can activate the CRISPR/Cas12a system and trigger its trans -cleavage activity. We reveal that the activated crRNA–Cas12a RNP favors the trans -cleavage of longer sequences than commonly used. These new findings of the RNA-activated trans -cleavage capability of Cas12a provided the foundation for the design and construction of CRISPR nanorobots that operate in living cells. We assembled the crRNA–Cas12a RNP and nucleic acid substrates on gold nanoparticles to form CRISPR nanorobots, which dramatically increased the local effective concentration of the substrate in relation to the RNP and the trans -cleavage kinetics. Binding of the target microRNA to the crRNA–Cas12a RNP activated the nanorobots and their trans -cleavage function. The repeated (multiple-turnover) trans -cleavage of the fluorophore-labeled substrates generated amplified fluorescence signals. Sensitive and real-time imaging of specific microRNA in live cells demonstrated the promising potential of the CRISPR nanorobot system for future applications in monitoring and modulating biological functions within living cells.
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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 | 0.000 | 0.000 |
| 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".