Dissecting bacterial heterogeneity using fluorescence and imaging mass spectrometry
EMSL Project ID
50492
Abstract
In nature, most microbes exist as members of complex multispecies communities that play vital roles promoting plant and soil health. Many of these microbial communities exist as biofilms: structured multispecies collections of microbial cells encased in a self-produced extracellular matrix. Such biofilms are important for processes such as bacterial colonization of plant roots and for adherence to biomass during degradation. Bacterial heterogeneity and cellular 'division of labor' is a hallmark of biofilms, and secreted specialized metabolites have been demonstrated to be key factors regulating bacterial cellular differentiation. However, even in well-studied model systems, we still have a poor understanding of how secreted metabolites spatially or temporally correlate with, or lead to the generation of, differentiated cell types within native biofilms. This is true even in biofilms generated by a single organism, let alone those created by multiple microbial species. We propose to address this fundamental question about how intra-species signaling influences bacterial differentiation at the single-cell and population levels using the model microbe, Bacillus subtilis. This plant-growth-promoting rhizobacterium and soil saprophyte also is highly genetically tractable and has long served as a model system to study biofilm formation and cellular heterogeneity. We will utilize the high-resolution chemical and fluorescence imaging available at EMSL to monitor the in situ localization of metabolites and their impacts on cellular organization and gene expression. We anticipate that these approaches will allow us to comprehensively elucidate gene expression patterns of B. subtilis subpopulations within biofilms and define the extent to which intraspecies cell-cell communication impacts the spatiotemporal dynamics of cellular heterogeneity. Achieving an understanding of how microbial differentiate in response to self-generated cues will lead to a better understanding of natural cell-cell interaction networks, allow the creation of models of biological communities, and enable better predictions about microbial behavior in natural environments.
Project Details
Project type
Exploratory Research
Start Date
2018-10-21
End Date
2020-03-31
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members