Microbial populations display heterogeneous gene expression profiles that result in phenotypic differences between individual bacteria. This diversity can allow populations to survive under uncertain and fluctuating conditions such as sudden antibiotic exposure, divide costly functions across different subpopulations, and enable interactions between different phenotypes. In addition to the temporal phenotypic heterogeneity seen in planktonic cultures, microbial populations and communities often exist in multicellular biofilms that exhibit considerable heterogeneity at the microscale, both in the local physicochemistry that individuals experience and in the species composition in their neighborhoods. Phenotypic and microscale variation represent central features of microbial populations, but the landscape of possible cellular states, their spatiotemporal regulation, and their roles in many biological phenomena are still largely unknown.
The microscale heterogeneity that defines microbial life can play important roles in community organization and function, including in antibiotic resistance and virulence. However, our understanding of these basic features has been limited by our ability to capture this heterogeneity at the relevant spatiotemporal scales. Overcoming these limitations could lead to new insights into the inner workings of microbial assemblages.
Researchers from the California Institute of Technology have developed par-seqFISH (parallel sequential fluorescence in situ hybridization), a high-throughput method that captures gene expression profiles of individual bacteria while also preserving their physical context within spatially structured environments. The researchers applied this approach to the study of Pseudomonas aeruginosa, a model biofilm-forming bacterium and an opportunistic human pathogen. Focusing on a set of 105 marker genes representing key aspects of P. aeruginosa physiology and virulence, they explored the transcriptional profiles of >600,000 bacteria across dozens of growth conditions. They uncovered a diverse set of metabolic- and virulence-related cellular states and quantified their temporal dynamics during population growth. In addition to recording gene expression, the researchers also demonstrated that par-seqFISH captures cell biological parameters such as cell size and can be further integrated with specific dyes to measure features such as chromosome copy in the same cells. Applying par-seqFISH to developing P. aeruginosa biofilms, they exposed the biogeographic context of cellular states, providing new insights into the spatial expression of various genes. These included, among other things, mutually exclusive expression patterns of flagella and type IV pili genes and a localized induction of pyocins, which are involved in kin selection and extracellular DNA release. Looking more closely, the researchers found that pyocin-encoding transcripts strongly localized to the bacterial cell poles. In early biofilms, they identified extensive microscale phenotypic structuring in which anaerobic metabolic processes such as denitrification appeared to locally influence the microenvironment through byproduct production. In more mature biofilms, they detected larger-scale partitions into zones of differential metabolic activities and virulence factor biosynthesis.
Transcriptional states of individual bacterial cells were identified using clustering analysis (left). The detected cellular states are depicted in different colors. Cell metabolic states can be directly mapped to their native biofilm context to identify emerging microenvironment dynamics (right).
Transcriptome imaging using par-seqFISH captures the microscale phenotypic variation of free-living and sessile bacterial populations. Caltech researchers report extensive heterogeneity in growing P. aeruginosa populations and demonstrate that individual multicellular biofilms can contain coexisting but separated subpopulations with distinct physiological activities. This multiplexed and spatially resolved method offers a generalizable tool for studying bacterial populations in space and time directly in their native contexts. Future applications in natural and clinical samples will provide insights into the conditions experienced by microbes in complex environments and the coordinated physiological responses that emerge in turn and reshape them.