RAPID: Evaluation of Cas12a Protospacer Nicking and Chimeric Reporters for PAM-free RNA and DNA diagnostics
Bibliographic record
Abstract
Abstract CRISPR-Cas nucleases have revolutionized diagnostics and biotechnology by providing programmable specificity. Here, we extend the understanding of Cas12a biology with a screen that, unexpectedly, finds that Cas12a trans cleavage activity can be modulated by nicks in the protospacer in a position-dependent manner. Wanting to explore the impact of non-conventional trans cleavage substrates, we subsequently find that non-specific Cas12a cleavage can be significantly reduced with RNA and chimeric (mixed RNA/DNA) reporter sequences. Exploiting these features and building on emerging PAM-independent Cas12a diagnostics that use engineered DNA activators and split-guide architectures, we introduce RAPID ( R NA/DNA A dvanced chimeric, P AM-independent, I ntegrated Nicking, D iagnostics), a nick-tuned, PAM-duplex-mediated platform for PAM-independent RNA and DNA detection. By strategically introducing a nick within the spacer region, RAPID expands Cas12a detection to include target RNAs, which can be ligated in situ to create a hybrid protospacer-target with trans cleavage activity matching conventional Cas12a. We then apply RAPID to detect single point mutations in ssDNA and RNA substrates, a challenge for traditional Cas12 and Cas13 systems. In combination with RT-LAMP, RAPID is used for PAM-independent RNA detection in clinical samples, achieving sensitivity down to ∼1 aM and 100% concordance with RT-qPCR for samples with Ct ≤33.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".