Cross-kingdom interactions

Profiling phage infection on a single-cell level

Bacteriophages are increasingly explored as tools for controlling bacterial populations in medicine, industry, and the environment. However, bacteria rapidly evolve resistance to phage infection, limiting the effectiveness of these approaches. In this work, we demonstrate that phage resistance in Bacteroides fragilis is not determined by a single phenotype, but instead emerges from combinations of measurable cellular states. By quantitatively profiling natural phenotypic variation across a bacterial population using microSPLiT, we are able to construct a phenotypic landscape that links cell state to phage infection outcomes. This landscape explains the robustness and rapid emergence of phage-resistant subpopulations without requiring genetic change.

Our findings show that phenotypic state is a major determinant of phage-host interactions in the gut. Understanding this landscape is critical for predicting phage dynamics in complex microbial communities and for designing phage-based interventions that remain effective against heterogenous bacterial populations.

Combinatorial phenotypic landscape enables bacterial resistance to phage infection

Led by: Anika Gupta, Dmitry Sutormin

Variability and noise during intracellular bacterial infection

Even in a lab-grown monoculture, bacterial gene expression can be highly heterogeneous. Many mammalian cell types also exhibit considerable variability in gene expression states. Thus, the interactions between individual pathogenic bacteria and host cells tend to lead to highly variable outcomes. The outcome of the resulting infection on a cellular level may range from complete clearance of infection to persistent survival of intracellular bacteria. We aim to explain and predict the outcomes of intracellular infection at the level of individual interacting cells based on the simultaneous high-resolution measurements of both host and pathogen gene expression states. To this end, we develop custom dual host-pathogen single-cell sequencing technologies.

Led by: Jackie Haring