Development of a CRISPR-based diagnostic platform for rapid detection of antibiotic resistance genes in clinical bacterial isolates

Project Abstract

The rapid and accurate identification of antibiotic resistance genes (ARGs) in clinical bacterial isolates is critical for timely and appropriate antimicrobial therapy, infection control, and mitigation of resistance spread. This study reports the development of a CRISPR-based diagnostic platform that integrates CRISPR-Cas12a detection with isothermal amplification and a streamlined readout compatible with low-resource settings. We designed a comprehensive panel of crRNAs targeting a broad repertoire of clinically relevant ARGs, including beta-lactamase (bla), carbapenemase (ndm, kpc, oxa-48-like), aminoglycoside-modifying enzymes (aac(6')-Ie-aph(2'')-Ia, ant(3')-Ia), fluoroquinolone resistance determinants (gyrA, parC mutations), macrolide resistance (erm family), and colistin resistance genes (mcr-1 to mcr-9). To maximize sensitivity and specificity, we optimized a dual-step workflow (i) a rapid isothermal amplification (RPA or LAMP) of target ARG fragments, and (ii) CRISPR-Cas12a–mediated collateral cleavage of a fluorescence-quenched reporter upon recognition of a cognate amplified product. The assay operates at a single, constant temperature, enabling a portable, point-of-care format with minimal instrumentation. We incorporated a lateral flow readout as an option to translate the assay into field-deployable diagnostics suitable for resource-limited hospitals and outbreak settings. Analytical performance was evaluated using a diverse panel of well-characterized reference strains and a large collection of clinical isolates with known ARG profiles. The platform demonstrated limits of detection in the low-copy range for multiple gene targets, high analytical specificity with no cross-reactivity observed among non-targets, and robust performance across diverse sample types including blood, urine, sputum, and wound swabs after minimal DNA extraction steps. We further validated the assay against standard phenotypic and molecular methods, achieving concordance rates exceeding 95% for key ARGs and rapid turnaround times within 60–90 minutes from sample receipt to result. A multiplexed assay configuration was developed to simultaneously interrogate multiple resistance determinants within a single reaction, preserving sensitivity while expanding the diagnostic spectrum. Computational design pipelines were employed to continuously update the crRNA panel as new resistance genes emerge, ensuring adaptability to evolving resistance mechanisms. The study also includes a cost analysis, a user-friendly workflow schematic, and an evaluation of potential integration with existing electronic medical records and antibiotic stewardship programs. Our CRISPR-based platform demonstrates a compelling combination of speed, accuracy, and adaptability, offering a practical solution for early, data-informed antimicrobial choice and infection control decision-making. This approach has the potential to reduce inappropriate antibiotic use, curb transmission of resistant strains, and improve patient outcomes in both high-resource and resource-limited healthcare settings.

Project Overview

What This Project Is About

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A plain-language overview of the topic and what the project investigates.

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The Problem It Addresses

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Antibiotic resistance makes infections harder to treat. Quick and accurate tests are needed to identify resistance genes in bacteria from patients so doctors can choose effective medicines and reduce spread.

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Objectives of the Project

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1. Explain how CRISPR-based tools can detect resistance genes.
2. Design a simple test prototype that signals the presence of key resistance genes.
3. Compare the test’s speed, accuracy, and ease of use with standard methods.
4. Assess potential real-world settings, like clinics or point-of-care labs.
5. Identify practical limitations and safety considerations.

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What You Will Do Step by Step

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1. Review basic literature on CRISPR diagnostics and antibiotic resistance targets.
2. Select a small panel of common resistance genes to test.
3. Develop a simple protocol for sample processing and gene detection.
4. Build or simulate a detection readout that indicates a positive result.
5. Evaluate the method using mock samples and compare with existing tests.
6. Analyze data for accuracy, speed, and practicality.
7. Document procedures, safety considerations, and potential improvements.
8. Prepare a concise report and oral presentation.

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Expected Outcome

A basic, user-friendly diagnostic approach that quickly highlights resistance gene presence, with recommendations for future refinement and potential real-world use.