Infectious diseases are caused by bacteria which involve in the complex interactions between microbial pathogens and host cells. Historically, studies of host–pathogen interactions relied on bulk measurements (e.g., population-level transcriptomics, flow cytometry of mixed cells, or colony counts), which average signals over millions of cells. Such bulk assays obscure critical cell-to-cell differences and rare subpopulations. In fact, both microbial populations and host immune cells are intrinsically heterogeneous: genetically identical bacteria can exhibit striking phenotypic variation (e.g., in metabolism, virulence gene expression, or stress response), and clonal immune cells adopt diverse activation states during infection. Single-cell techniques overcome these limitations by profiling individual cells, revealing distinct subsets of host or pathogen cells. Recent advances in single-cell genomics, transcriptomics, proteomics, and imaging now enable the capture of the full diversity of infection processes at unprecedented resolution. This chapter reviews how single-cell methods have transformed the study of bacterial infections by uncovering hidden heterogeneity, mapping host–pathogen dynamics, and revealing spatial and temporal patterns in microbial communities and immune responses.