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Biomedical Imaging and Instrumentation

Biomedical Imaging and Instrumentation - Poster Session E

Poster G4 - Validation of Cas13a-based detection method of RNA viruses in Wastewater Diagnostics

Saturday, October 26, 2024

10:00 AM – 11:00 AM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

AZ

Ahmad Zia

Student
University of Central Arkansas
Conway, Arkansas, United States

Co-Author(s)

DM

Daniel Maxenberger

Co-author
University of Arkansas, United States

Primary Investigator(s)

CN

Christopher Nelson, PhD

Assistant Professor
University of Arkansas
Fayetteville, Arkansas, United States

Introduction: Rapid and accurate detection of nucleic acids is crucial for addressing global health challenges such as infectious disease outbreaks and antimicrobial resistance. Traditional methods for nucleic acid amplification and detection often require specialized equipment and trained personnel, limiting their applicability in resource-constrained settings. This has driven the development of rapid, portable, and user-friendly diagnostic tools. CRISPR-based technologies, including specific high-sensitivity enzymatic reporter unlocking (SHERLOCK), offer a promising platform for sensitive and specific nucleic acid detection. By leveraging the programmable specificity of CRISPR-Cas enzymes, SHERLOCK has the potential to revolutionize diagnostic testing. This platform uses CRISPR-Cas enzymatic activity to detect specific nucleic acid sequences, enabling the identification of pathogens, genetic mutations, and other biological targets. Similar to other diagnostic technologies, SHERLOCK's efficacy hinges on efficient and accurate nucleic acid amplification. Recombinase Polymerase Amplification (RPA) is a key component of the SHERLOCK workflow, serving as a rapid and isothermal method for amplifying target nucleic acids. Our research aims to develop a standardized protocol which we can use to detect RNA viruses in wastewater. By systematically evaluating different parameters we seek to establish optimal conditions for efficient and robust nucleic acid detection. Ultimately, this research will contribute to the development of more reliable and superior diagnostic tools based on the SHERLOCK platform.

Materials and

Methods: The SHERLOCK platform, combining RPA and CRISPR-Cas13 collateral nuclease activity, was employed for nucleic acid detection. RPA conditions were systematically refined and optimized for enhanced sensitivity and specificity, evaluating parameters like temperature, buffer composition, and enzyme concentration. Primers targeting the GFP expression portion of a GFP-expressing Lentiviral genome were optimized using purified RNA from the virus sample and plasmid DNA containing the Lentiviral genome. TwistDx RPA reagents were employed in a design-of-experiment (DOE) approach, with quantification of amplification products. Reaction conditions varied across defined ranges for various parameters. Optimized RPA conditions were integrated into SHERLOCK to evaluate assay performance, assessing analytical sensitivity, specificity, and LOD for the nucleic acid targets. Gel electrophoresis verified RPA efficiency and specificity by utilizing both positive and negative controls to test the accuracy of each. After establishing that the RPA amplified the target sequence; the detection mechanism involved Cas13a protein, crRNAs, T7 RNA polymerase, RNAse inhibitors, and an RNA probe fluorescing upon Cas13 collateral activity. We imaged the samples using a 384-well plate. The RNA probe fluoresces only when the collateral catalytic activity of Cas13 was unleashed after finding the target RNA. Fluorescence was monitored over three hours using a plate reader.

We had to break this protocol into several sections to troubleshoot certain aspects of the experiments that kept failing. However, we were finally able to create a robust and precise protocol which we now hope to use wastewater samples and, in the future, enable its broader application in diagnostic settings.

Results, Conclusions, and Discussions: Optimization of RPA reaction conditions resulted in significantly enhanced amplification efficiency and specificity for the target nucleic acid panel. Optimal parameters included a reaction temperature of 39°C, a specific buffer composition, and an enzyme concentration of 2U/µL. These optimized conditions consistently yielded robust amplification curves with high Ct values in negative controls, indicating low levels of background amplification.
The SHERLOCK assay incorporating the optimized RPA conditions demonstrated high sensitivity and specificity for all tested nucleic acid targets. The limit of detection (LOD) was determined to be as low as 10 copies/µL for most targets, with a dynamic range spanning several orders of magnitude. No cross-reactivity was observed with closely related sequences, highlighting the assay’s specificity.
The successful optimization of RPA conditions significantly improved the performance of the SHERLOCK platform. Theresulting high sensitivity and specificity are crucial for accurate and reliable nucleic acid detection. These findings demonstrate the potential of SHERLOCK as a versatile diagnostic tool for various applications.
The next phase of this research will focus on applying the optimized SHERLOCK protocol to wastewater samples. This will enable the detection and quantification of target nucleic acids in a complex environmental matrix, providing valuable insights into the prevalence and dynamics of pathogens or other biological contaminants.
Further optimization of the SHERLOCK platform may include exploring different CRISPR-Cas enzyme variants and developing multiplex assays for simultaneous detection of multiple targets. Additionally, the integration of SHERLOCK with point-of-care devices could expand its accessibility and utility in resource-limited settings.
While this study focused mainly on optimizing RPA conditions, future research should investigate other factors influencing SHERLOCK performance, such as sample preparation and nucleic acid extraction methods. A comprehensive understanding of these variables will contribute to the development of robust and standardized SHERLOCK-based diagnostic assays.
Overall, the results presented in this study provide a strong foundation for the development of SHERLOCK as a powerful and versatile diagnostic platform which we now hope to use for its broader applications.

Acknowledgements (Optional): This work was made possible by the University of Arkansas Engineering Research Innovation Seed Grant, the 21st Century Chair of Biomedical Engineering, and the NSF REU Site: Training in Emerging Biomedical optics and Imaging Approaches (2243953).