High-throughput single-cell RNA sequencing of bacteria to uncover cell states involved in pathogenesis - Bacterial cells with the same genetic material can have different transcriptional states. These distinct transcriptional states are crucial to many important physiological functions, such as sporulation, motility, metabolic adaptation, antibiotic resistance, and biofilm formation. To identify the distinct transcriptional cell states that are present in a population of bacteria, we need to measure the expression levels of all genes in individual bacterial cells across a large number of cells. To do so, we have recently developed a method, called proBac- seq, which extends the power of commercial microfluidic platforms for single-cell RNA sequencing of mammalian cells to bacterial cells. Our method uses DNA probes with poly-A tails to tag individual transcripts in fixed bacteria. The tagged bacteria are then processed with a commercial platform for single-cell RNA sequencing (such as 10X) where the DNA probes are captured and quantified as if they were transcripts of a mammalian cell. In proof- of-principle experiments, we have applied our method to E. coli, Bacillus, and C. perfringens, identifying known and new transcriptional cell states. Here, we propose to extend proBac-seq to enable profiling of hundreds of thousands of individual bacterial cells in a single run and apply it to create an atlas of transcriptional cell states of Salmonella enterica during infection. Salmonella is an important human pathogen and a major cause of foodborne illnesses. Intriguingly, pathogenic strains of Salmonella often exist asymptomatically in people. In addition, Salmonella can infect a wide variety of cell types, colonize different niches, and interact with the immune cells and gut microbiome. Therefore, to understand pathogenicity of Salmonella and devise effective therapies, we need to identify what cell states are present during infection, how cells transition between these states, and at what rates. To answer this question, we will develop a high through-put platform for cost-effective single-cell RNA sequencing of bacteria (Aim 1). We will incorporate multiplexing into proBac-seq to increase cell numbers, implement protein readout using DNA-tagged antibodies, and develop a method for enriching for a particular species of bacteria from a mixture of species prior to single-cell profiling. Critically, we will build the computational frameworks needed for data analysis. Our platform will be broadly useful. We will therefore openly share reagents, protocols, and computational pipelines. In Aim 2, we will apply our platform to create an atlas of transcriptional cell states of Salmonella during infection in culture and using an in vitro model of intestinal organoids. We will infer how distinct cell states interact with each other, how they are regulated, and the dynamics of transitions between them. We will identify perturbations that disrupt the virulent cell state or hinder transitions into that state. In Aim 3, we will validate our findings in an in vivo model of infection using gnotobiotic mice. We will also measure the interactions between the gut microbiome and cell states of Salmonella using gnotobiotic mice colonized with naturally acquired microbiota. Our findings can potentially transform our understanding of pathogenicity of Salmonella. The tools developed here can be broadly applied to other bacterial systems.
