CRISPR/Cas12a2 enables ultra-sensitive amplification-free RNA detection
Bibliographic record
Abstract
Advances in clustered regularly interspaced short palindromic repeat (CRISPR) technologies have led to the use of diverse CRISPR-associated (Cas) proteins in diagnostic applications. Herein, we present a CRISPR/Cas12a2-based amplification-free RNA detection method that exhibits sub-attomolar sensitivity and substrate versatility. Cas12a2, a recently characterized RNA-guided nuclease, uniquely integrates bimolecular recognition through CRISPR RNA (crRNA)-target complementarity and protospacer flanking sequence identification, enabling highly specific trans-cleavage of single-stranded DNA, double-stranded DNA, and RNA. We have optimized key biochemical parameters, including pH, ionic strength, and temperature, to enhance the catalytic efficiency of Cas12a2. Based on the optimal activity conditions of Cas12a2, we have achieved ultra-sensitive viral RNA detection with a limit of detection of 46.7 aM through the strategic design and cooperative activation of crRNAs targeting conserved regions of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome. The diagnostic accuracy of the Cas12a2-based assay has been demonstrated for 26 SARS-CoV-2 variants, and it has further resulted in the definitive diagnosis of 317 clinical samples. This work establishes Cas12a2 as a promising molecular diagnostic tool that provides an amplification-free, rapid, and versatile solution for RNA detection. The adaptability and simplicity of the platform render it particularly well suited for point-of-care applications, paving the way for next-generation CRISPR diagnostics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".