The CRISPR–Cas toolbox for analytical and diagnostic assay development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated (Cas) systems have revolutionized biological and biomedical sciences in many ways. The last few years have also seen tremendous interest in deploying the CRISPR-Cas toolbox for analytical and diagnostic assay development because CRISPR-Cas is one of the most powerful classes of molecular machineries for the recognition and manipulation of nucleic acids. In the short period of development, many CRISPR-enabled assays have already established critical roles in clinical diagnostics, biosensing, and bioimaging. We describe in this review the recent advances and design principles of CRISPR mediated analytical tools with an emphasis on the functional roles of CRISPR-Cas machineries as highly efficient binders and molecular scissors. We highlight the diverse engineering approaches for molecularly modifying CRISPR-Cas machineries and for devising better readout platforms. We discuss the potential roles of these new approaches and platforms in enhancing assay sensitivity, specificity, multiplexity, and clinical outcomes. By illustrating the biochemical and analytical processes, we hope this review will help guide the best use of the CRISPR-Cas toolbox in detecting, quantifying and imaging biologically and clinically important molecules and inspire new ideas, technological advances and engineering strategies for addressing real-world challenges such as the on-going COVID-19 pandemic.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it