Authors
Giorgio, R. T., Zhang, T., Holmes, C. L., Hullahalli, K.
Abstract
Bacterial dissemination across tissues is a critically important process influencing infection outcomes. Monitoring within-host dissemination is challenging because conventional measures of bulk bacterial burden cannot distinguish between lineages that are shared between tissues and those that replicate locally. This limitation can be overcome using barcoded bacteria, where deep sequencing of the barcode locus and comparisons of barcodes between tissues define which lineages spread within the host. Numerous studies have used barcoded bacteria to generate high-resolution maps of dissemination. However, since multiple cells in the infectious inoculum can contain identical barcodes, inferences about dissemination can be confounded when distinct lineages from the inoculum with identical barcodes are observed in different tissues. Thus, even though the same barcodes can be observed in different tissues, dissemination between these tissues may not have occurred. Here, we aimed to develop an approach that would provide a solution to this confounding effect. We developed a simulation-based distance metric that quantifies the significance of observing shared barcodes between tissues. We validated this approach using simulated datasets spanning three orders of magnitude in barcode diversities and on three published experimental infection datasets. Our reanalysis reveals previously unappreciated patterns of Escherichia coli spread during liver abscess formation, clarifies the role of the Muc2 mucin in Listeria monocytogenes systemic spread, and quantifies how Klebsiella pneumoniae replication in the lungs drives systemic dissemination. As barcoding studies expand across diverse infection models, this approach provides an essential tool for accurate interpretation of within-host bacterial dissemination.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 12 Nov 2025.
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