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Infection with Mycobacterium tuberculosis causes a spectrum of outcomes; the majority of individuals contain but do not eliminate the infection, while a small subset present with primary active tuberculosis (TB) disease. This variability in infection outcomes is recapitulated at the granuloma level within each host, such that some sites of infection can be fully cleared while others progress. Understanding the spectrum of TB outcomes requires new tools to deconstruct the mechanisms underlying differences in granuloma fate. Here, we use novel genome-encoded barcodes to uniquely tag individual M. tuberculosis bacilli, enabling us to quantitatively track the trajectory of each infecting bacterium in a macaque model of TB. We also introduce a robust bioinformatics pipeline capable of identifying and counting barcode sequences within complex mixtures and at various read depths. By coupling this tagging strategy with serial positron emission tomography coregistered with computed tomography (PET/CT) imaging of lung pathology in macaques, we define a lesional map of M. tuberculosis infection dynamics. We find that there is no significant infection bottleneck, but there are significant constraints on productive bacterial trafficking out of primary granulomas. Our findings validate our barcoding approach and demonstrate its utility in probing lesion-specific biology and dissemination. This novel technology has the potential to greatly enhance our understanding of local dynamics in tuberculosis. IMPORTANCE Classically, M. tuberculosis infection was thought to result in either latent infection or active disease. More recently, the field has recognized that there is a spectrum of M. tuberculosis infection clinical outcomes. Within a single host, this spectrum is recapitulated at the granuloma level, where there can simultaneously be lesional sterilization and poorly contained disease. To better understand the lesional biology of TB infection, we digitally barcoded M. tuberculosis to quantitatively track the fate of each infecting bacterium. By combining this technology with serial PET-CT imaging, we can dynamically track both bacterial populations and granuloma trajectories. We demonstrate that there is little constraint on the bacterial population at the time of infection. However, the granuloma imposes a strong bottleneck on dissemination, and the subset of granulomas at risk of dissemination can be distinguished by physical features.

Digitally Barcoding Mycobacterium tuberculosis Reveals In Vivo Infection Dynamics in the Macaque Model of Tuberculosis