A new CRISPR-based diagnostic platform can identify 15 clinically relevant mycobacterial species within hours, potentially overcoming the delays and limitations of conventional culture-based testing.
The technology, known as CRISPR-assisted nanodroplet differential identification (CANDI), was developed by Gou and colleagues and is discussed in a recent commentary published in LabMed Discovery.
The approach addresses a growing diagnostic challenge: distinguishing tuberculosis from infections caused by nontuberculous mycobacteria (NTM). Although tuberculosis remains a major global health concern, NTM infections are increasingly recognized as a cause of pulmonary disease.
Because these infections can produce similar symptoms, accurate species identification is essential for selecting appropriate treatment. However, conventional culture-based methods can take days to weeks, while existing molecular tests may struggle to distinguish closely related organisms or identify multiple species in one specimen.
CANDI combines broad-range DNA amplification with CRISPR-based molecular recognition. Rather than requiring separate amplification reactions for individual species, the platform first amplifies genetic regions shared across mycobacteria. Species-specific CRISPR reactions then identify differences between organisms.
These reactions take place in tiny droplets, each labeled with a distinct fluorescent signal, allowing multiple species to be identified simultaneously.
In the reported evaluation, CANDI successfully identified mixtures containing up to five mycobacterial species, including closely related organisms within the Mycobacterium abscessus and Mycobacterium avium complexes.
Clinical testing also demonstrated high agreement with culture-based identification, while detecting some organisms missed by conventional methods.
For diagnostic laboratories, earlier species-level identification could support more timely clinical assessment and treatment decisions, particularly in patients with suspected mixed infections or immunocompromised patients.
However, the authors emphasize that molecular detection alone cannot distinguish active infection from colonization. Results must therefore be interpreted alongside clinical findings.
Practical barriers also remain. The current workflow requires manual droplet preparation, chip loading, and fluorescence microscopy, potentially limiting throughput and standardization.
Further automation and validation will be necessary before routine clinical implementation, but the approach offers a promising route toward faster, more comprehensive mycobacterial diagnostics.
Newsletters
Receive the latest pathologist news, personalities, education, and career development – weekly to your inbox.
