Spying on microbial communities, cell by cell
Within any community of organisms, gene expression is heterogeneous, which can manifest in genetically identical individuals having a different phenotype. One has to look at individuals in context and analyze patterns in both space and time to see the full picture. Aiming to fill a gap in current methods, Dar et al. developed a transcriptome-imaging method named parallel sequential fluorescence in situ hybridization (par-seqFISH). They applied this technique to the opportunistic pathogen Pseudomonas aeruginosa, focusing on biofilms where growth conditions can change at microscopic scale. Development of these communities, as revealed by mRNA composition, were followed in space and time. The results revealed a heterogeneous phenotypic landscape, with oxygen availability shaping the metabolism at a spatial scale of microns within a single contiguous biofilm segment. This tool should be applicable to complex microbial communities in the environment and the human microbiome.
Science, abi4882, this issue p. eabi4882
Structured Abstract
INTRODUCTION
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.
RATIONALE
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.
RESULTS
We 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. We 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, we explored the transcriptional profiles of >600,000 bacteria across dozens of growth conditions. We 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, we 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, we 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, we found that pyocin-encoding transcripts strongly localized to the bacterial cell poles. In early biofilms, we 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, we detected larger-scale partitions into zones of differential metabolic activities and virulence factor biosynthesis.
CONCLUSION
Transcriptome imaging using par-seqFISH captures the microscale phenotypic variation of free-living and sessile bacterial populations. We 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.
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).
” data-icon-position=”” data-hide-link-title=”0″>
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).
Abstract
Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.