Research ArticleTuberculosis

Rapid and specific labeling of single live Mycobacterium tuberculosis with a dual-targeting fluorogenic probe

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Science Translational Medicine  15 Aug 2018:
Vol. 10, Issue 454, eaar4470
DOI: 10.1126/scitranslmed.aar4470

Tracking tuberculosis

Tuberculosis, a bacterial infection of the lungs, continues to plague countries worldwide. Cheng et al. developed an imaging probe specific for Mycobacterium tuberculosis. The probe fluoresces upon activation by an enzyme in the bacteria, and fluorescence is retained by modification of a second bacterial enzyme required for formation of the bacterial cell wall. The probe could identify single live tuberculosis bacteria from nontuberculosis bacteria and dead bacteria, and was compatible with patient sputum samples. The authors also developed a microfluidic chip to aid in automating live tuberculosis bacterial counts from sputum samples. This probe and chip platform could aid in drug testing and diagnosis.


Tuberculosis (TB) remains a public health crisis and a leading cause of infection-related death globally. Although in high demand, imaging technologies that enable rapid, specific, and nongenetic labeling of live Mycobacterium tuberculosis (Mtb) remain underdeveloped. We report a dual-targeting strategy to develop a small molecular probe (CDG-DNB3) that can fluorescently label single bacilli within 1 hour. CDG-DNB3 fluoresces upon activation of the β-lactamase BlaC, a hydrolase naturally expressed in Mtb, and the fluorescent product is retained through covalent modification of the Mtb essential enzyme decaprenylphosphoryl-β-d-ribose 2′-epimerase (DprE1). This dual-targeting probe not only discriminates live from dead Bacillus Calmette-Guérin (BCG) but also shows specificity for Mtb over other bacterial species including 43 nontuberculosis mycobacteria (NTM). In addition, CDG-DNB3 can image BCG phagocytosis in real time, as well as Mtb in patients’ sputum. Together with a low-cost, self-driven microfluidic chip, we have achieved rapid labeling and automated quantification of live BCG. This labeling approach should find many potential applications for research toward TB pathogenesis, treatment efficacy assessment, and diagnosis.

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