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<p>Tuberculosis (TB), caused by <i>Mycobacterium tuberculosis</i> (Mtb), remains a major global health challenge due to persistent diagnostic gaps. CRISPR–Cas–based diagnostics have emerged as highly sensitive and programmable platforms for nucleic acid detection, enabling rapid identification of Mtb targets, including drug-resistance–associated mutations. These systems integrate isothermal amplification, diverse Cas effectors, and multiple signal readout strategies to achieve high analytical performance. This review provides a comparative analysis of clinically evaluated CRISPR-based TB diagnostic platforms, highlighting substantial variability in assay design, performance, and translational readiness. While many platforms demonstrate strong analytical sensitivity, their implementation remains constrained by workflow complexity and limited integration into true point-of-care formats. This highlights that successful clinical translation of CRISPR-based TB diagnostics is determined more by real-world adaptability than by analytical performance alone. The current review presents a comparative analysis of CRISPR-based diagnostic platforms for tuberculosis, evaluating the variability in assay design, analytical and clinical performance, and translational readiness across currently available systems.</p> Graphical abstract <p>Overview of CRISPR–Cas–based diagnostic platforms for Mycobacterium tuberculosis, illustrating the workflow from sample collection and nucleic acid processing to isothermal amplification, target-specific detection using Cas12/Cas13 systems, and diverse signal readouts (lateral flow, fluorescent, and biosensor-based). Despite strong analytical performance, clinical utility remains limited by sample preparation requirements, workflow complexity, and incomplete integration into deployable point-of-care systems. Created with BioRender.com.</p> <p></p>

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CRISPR–Cas-based detection of Mycobacterium tuberculosis: current advances and translational bottlenecks

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Nour Akkoul,
  • Tarun Kumar,
  • Swaati Sharma,
  • Vipasha Sharma,
  • Vaseem Raja,
  • Tanzil Gaffar Malik,
  • Wan Shafrina Wan Mohd Jaafar,
  • Ajay Kumar Gupta,
  • Khalid Rehman Hakeem

摘要

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global health challenge due to persistent diagnostic gaps. CRISPR–Cas–based diagnostics have emerged as highly sensitive and programmable platforms for nucleic acid detection, enabling rapid identification of Mtb targets, including drug-resistance–associated mutations. These systems integrate isothermal amplification, diverse Cas effectors, and multiple signal readout strategies to achieve high analytical performance. This review provides a comparative analysis of clinically evaluated CRISPR-based TB diagnostic platforms, highlighting substantial variability in assay design, performance, and translational readiness. While many platforms demonstrate strong analytical sensitivity, their implementation remains constrained by workflow complexity and limited integration into true point-of-care formats. This highlights that successful clinical translation of CRISPR-based TB diagnostics is determined more by real-world adaptability than by analytical performance alone. The current review presents a comparative analysis of CRISPR-based diagnostic platforms for tuberculosis, evaluating the variability in assay design, analytical and clinical performance, and translational readiness across currently available systems.

Graphical abstract

Overview of CRISPR–Cas–based diagnostic platforms for Mycobacterium tuberculosis, illustrating the workflow from sample collection and nucleic acid processing to isothermal amplification, target-specific detection using Cas12/Cas13 systems, and diverse signal readouts (lateral flow, fluorescent, and biosensor-based). Despite strong analytical performance, clinical utility remains limited by sample preparation requirements, workflow complexity, and incomplete integration into deployable point-of-care systems. Created with BioRender.com.