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Quantification of Mycobacterium tuberculosis Growth in Cell-Based Infection Assays by Time-Lapse Fluorescence
Chiara Toniolo1, Daniel Sage2, John D McKinney1
1School of Life Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland.
Methods in Molecular Biology (Clifton, N.J.)
|June 18, 2024
Summary
Quantifying Mycobacterium tuberculosis (Mtb) growth in vitro typically takes weeks. This study introduces a faster method using time-lapse microscopy for quicker insights into Mtb infection dynamics.
Area of Science:
- Microbiology
- Cell Biology
- Infectious Diseases
Background:
- Traditional quantification of Mycobacterium tuberculosis (Mtb) growth in vitro using colony-forming units (CFU) is time-consuming due to Mtb's slow doubling time (16-24 hours).
- This lengthy incubation period (≥3 weeks) can delay research progress in understanding Mtb infection dynamics.
Purpose of the Study:
- To present an alternative, faster method for quantifying Mtb growth dynamics in host cells.
- To enable simultaneous capture of multiple readouts from a single experimental setup.
Main Methods:
- Utilizing time-lapse microscopy for real-time observation of Mtb within host cells.
- Employing quantitative image analysis to process microscopy data.
- Developing an experimental approach to capture host cell viability and bacterial localization.
Main Results:
- The described approach significantly reduces the time required for Mtb growth quantification compared to traditional CFU methods.
- This method allows for the simultaneous assessment of bacterial growth, host cell viability, and infection spread dynamics.
- It provides insights into bacterial localization within host cells and the propagation of infection between cells.
Conclusions:
- Time-lapse microscopy and quantitative image analysis offer a rapid and comprehensive alternative for studying Mtb infection in vitro.
- This technique accelerates the investigation of Mtb pathogenesis and the evaluation of potential therapeutics.
- The method's ability to capture multiple parameters enhances its utility in cell-based infection models.

