Robust Inference of Phage Host Infection Dynamics from Sparse Single-Cell Transcriptomic Profiles in Pseudomonas aeruginosa
Wilms, H. T.; Gurney, J.
Show abstract
Phage therapy is providing a benefit to a limited number of people. To date those who have received phage therapy typically have done so via an IND expanded access mechanism, colloquially referred to as compassionate release, new clinical trials are underway, and the advent of phage therapy seems to be at hand. However, a recurring question remains, which phages to use for which bacteria. By understanding how a phage interacts with its bacterial host we will move closer to being able to provide an answer to this pressing question. By using bacterial single cell RNA sequencing (scRNAseq) during phage infection we can look at the most fundamental interaction between the phage and its host. We have observed phage gene expression consistent with well described expression profiles seen in bulk expression data. Phage genes in our scRNAseq data followed the early, mid, and late gene expression profile. Using a machine learning algorithm (Support Vector Machine) we were able to distinguish time of infection from phage genes. We also observed that phage infection is likely not random. When infected with two genetically distinct phages the likelihood of finding both inside a single cell was not consistent with Poisson distribution.
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