Evaluating antibiotic tolerance in single cells with tandem single cell measurements of translation rate and transcriptomes

Phenotypic tolerance to antibiotics is a major cause of antibiotic drug failure. Non-heritable mechanisms that confer antibiotic tolerance have long been associated with cells that maintain a diminished metabolic rate. The presence of a small number of cells in a low activity level within a large population of susceptible cells poses formidable technical challenges for analysis because any bulk measurement will average out the small signal produced by the tolerant cells with the strong signal of the larger and more active population. Single cell measurements that can identify the activity level of single cells and link them with the transcriptomic signature of these cells can solve this problem and address fundamental open questions about how these low activity cells arise and persist. For starters, such measurements can determine whether cells in this state all share a signature transcriptomic profile or if multiple different gene expression states are correlated with low activity. Additionally, measurements of transcriptomic activity in cells that have lower activity levels can predict what metabolic genes these cells express and pinpoint whether there are weaknesses that can be exploited therapeutically. In this proposal we aim to first develop a single cell technique that will report the translational rate and transcriptome of thousands of individual bacterial cells. We will then explore the level of translational and transcriptomic heterogeneity in Staphylococcus aureus cells that were passaged in a model that mimics chronic infections and long-term antibiotic recalcitrance. Our data will determine whether an increasing percent of the S. aureus population is found in a low-activity state upon continuous infection and the presence of antibiotics.