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Record W2966886623 · doi:10.1101/734582

A Universal Proximity CRISPR Cas12a Assay for Ultrasensitive Detection of Nucleic Acids and Proteins

2019· preprint· en· W2966886623 on OpenAlexaff
Yongya Li, Hayam Mansour, Yanan Tang, Feng Li

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsBrock University
Fundersnot available
KeywordsTrans-activating crRNACRISPRNucleic acidRNAPrimer (cosmetics)DeoxyribozymeComputational biologyChemistryPrimer extensionMolecular biologyDNABiologyBiochemistryCas9Gene

Abstract

fetched live from OpenAlex

Abstract Herein, we describe a proximity CRISPR Cas12a assay that harnesses “collateral” single-stranded DNase activity of Cas12a as a universal amplifier for the ultrasensitive detection of nucleic acids and proteins. The target recognition is achieved through proximity binding rather than recognition by CRISPR RNA (crRNA), which allows the flexible assay design and expansion to proteins. A binding-induced primer extension reaction is then used to generate a predesigned CRISPR-targetable sequence as a barcode for signal amplification. We demonstrate that our assay is highly sensitive and universal. As low as 1 fM nucleic acid target could be detected isothermally in a homogeneous solution via the integration with nicking cleavage. We’ve also successfully adapted the assay for the sensitive and wash-free detection of antibodies in both buffer and diluted human serum samples.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.008
GPT teacher head0.227
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicAdvanced biosensing and bioanalysis techniques→French-language works237,207→