Recent advances in electrochemical-based CRISPR/Cas biosensing for nucleic acid and non-nucleic acid pathogenic microorganism detection.
AI summary
75% confidenceThis MEC study investigates Biosensors, Detection, EC-CRISPR/Cas. The research focuses on system optimization and characterization.
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- REVIEW
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Abstract
The widespread presence of pathogenic microorganisms in food and environmental sources poses a persistent threat to public health. Conventional detection methods-including culture, microscopy, and biochemical assays-are limited by low sensitivity, cross-reactivity, and prolonged turnaround times, particularly when microbial loads are low or phenotypic overlap occurs. These limitations underscore the urgent need for diagnostic platforms that combine speed, specificity, and sensitivity. The advent of CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated) systems has revolutionized microbial diagnostics, driving the emergence of electrochemical CRISPR/Cas (EC-CRISPR/Cas) biosensors. This review surveys four principal electrochemical CRISPR/Cas (EC-CRISPR/Cas) platforms-Cas9, Cas12a, Cas13a, and Cas14a-emphasizing their structural characteristics, biosensing mechanisms, and signal amplification strategies for both nucleic acid and non-nucleic acid pathogen detection. We first outline the molecular architecture and functional mechanisms of each Cas protein in the context of biosensing. EC-CRISPR/Cas detection strategies are classified as nucleic acid-based (either amplification-free or amplification-dependent) or non-nucleic acid-based, the latter primarily relying on aptamer-mediated recognition. We also provide a comparative analysis of signal enhancement techniques and application scenarios across bacterial, viral, fungal, and parasitic pathogens. Importantly, we identify key limitations of current systems-such as poor reusability, signal drift, and challenges in point-of-care deployment-and present emerging solutions including crRNA engineering, nanomaterial integration, and artificial intelligence-guided biosensor design. These innovations hold strong potential to enhance sensitivity, specificity, and real-time performance, offering a foundation for next-generation, scalable EC-CRISPR/Cas diagnostics.