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In Situ Complexation of sgRNA and Cas12a Improves the Performance of a One-Pot RPA-CRISPR-Cas12 Assay.

Jake M LesinskiThomas MoraguesPrerit MathurYang ShenCarolina PaganiniLéonard BezingeBo VerberckmoesBodine Van EenoogheStavros StavrakisAndrew J deMelloDaniel A Richards
Published in: Analytical chemistry (2024)
Due to their ability to selectively target pathogen-specific nucleic acids, CRISPR-Cas systems are increasingly being employed as diagnostic tools. "One-pot" assays that combine nucleic acid amplification and CRISPR-Cas systems (NAAT-CRISPR-Cas) in a single step have emerged as one of the most popular CRISPR-Cas biosensing formats. However, operational simplicity comes at a cost, with one-pot assays typically being less sensitive than corresponding two-step NAAT-CRISPR-Cas assays and often failing to detect targets at low concentrations. It is thought that these performance reductions result from the competition between the two enzymatic processes driving the assay, namely, Cas-mediated cis -cleavage and polymerase-mediated amplification of the target DNA. Herein, we describe a novel one-pot RPA-Cas12a assay that circumvents this issue by leveraging in situ complexation of the target-specific sgRNA and Cas12a to purposefully limit the concentration of active Cas12a during the early stages of the assay. Using a clinically relevant assay against a DNA target for HPV-16, we show how this in situ format reduces competition between target cleavage and amplification and engenders significant improvements in detection limit when compared to the traditional one-pot assay format, even in patient-derived samples. Finally, to gain further insight into the assay, we use experimental data to formulate a mechanistic model describing the competition between the Cas enzyme and nucleic acid amplification. These findings suggest that purposefully limiting cis -cleavage rates of Cas proteins is a viable strategy for improving the performance of one-pot NAAT-CRISPR-Cas assays.
Keyphrases
  • crispr cas
  • nucleic acid
  • genome editing
  • high throughput
  • single cell
  • label free
  • machine learning
  • dna binding
  • transcription factor
  • artificial intelligence
  • nitric oxide
  • electronic health record