Advances in the Application of CRISPR/Cas Systems in Molecular Diagnostics

Abstract

Rapid advances in CRISPR/Cas systems and the growing global demand for rapid, accurate diagnostics underscore the necessity of reviewing how these technologies are transforming molecular testing. Conventional diagnostic approaches are frequently constrained by prolonged turnaround times, complex instrumentation, and limited analytical sensitivity, and these limitations were starkly highlighted during the COVID-19 pandemic. In this context, we present a comprehensive and timely overview of CRISPR/Casbased molecular diagnostics. We begin by summarizing the classification and molecular mechanisms of CRISPR/Cas types I-VI, followed by a detailed discussion of innovative detection strategies such as SHERLOCK, DETECTR, and amplification-free platforms that significantly enhance analytical sensitivity and specificity. We further explore clinical applications across infectious disease surveillance, antimicrobial resistance profiling, early cancer detection, genetic variant identification, and the emerging detection of nonnucleic acid biomarkers. Finally, we discuss future perspectives, including the development of miniaturized, high-throughput, and AI-assisted diagnostic platforms, their integration with microfluidics and portable readout systems for point-of-care applications, and highlight critical challenges such as standardization, automation, and cost-effectiveness that must be addressed to facilitate clinical translation.

Keywords

CRISPR/Cas system / Molecular diagnostics / Point-of-care testing (POCT) / Clinical translational application / Artificial intelligence-assisted molecular diagnosis

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the author(s) used Kimi and DeepSeek in order to assist with language editing and manuscript polishing. After using these tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Acknowledgments

The authors thank all colleagues who provided technical assistance and helpful discussions during the preparation of this manuscript.

Author Contributions

Conceptualization, K.W.; Writing—Original Draft Preparation, H.R. and K.W.; Writing—Review & Editing, S.L., Y.Y. and P.C.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No data was used for the research described in the article.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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