Single-cell transcriptomics of complex bacterial communities

Project Summary Gene expression in bacteria is heterogeneous even within genetically identical cells due to the stochastic activation of many gene regulatory programs. The resulting phenotypic diversity often plays an important functional role for bacterial communities, for example, facilitating horizontal gene transfer. This fundamentally single-cell behavior cannot be resolved with population level measurements and, so far, has been studied in pure cultures of genetically tractable organisms using low-throughput reporter-based assays. However, the majority of bacteria in nature reside in complex microbial communities spatially organized into biofilms and composed of multiple interacting members. Within such communities, a multitude of behaviors emerge from the dynamic interplay of noisy gene expression states and responses to the heterogeneous microenvironment. Absence of approaches for measuring phenotypic states within complex polymicrobial communities simultaneously at systems scale and with single-cell resolution results in a lack of mechanistic understanding of bacterial ecology and is therefore a critical barrier for the fields of microbiology and microbiome studies. During my postdoc, I developed a high-throughput bacterial single-cell transcriptomics technology, microSPLiT (microbial Split-Pool Ligation Transcriptomics), that allows to measure gene expression states in tens of thousands of individual cells using only common laboratory equipment. In my lab, I aim to further extend microSPLiT for single-cell transcriptomics of biofilms, as well as of complex bacterial consortia. Specifically, in my first project we will create a single-cell gene expression map of single- and dual-species biofilms of Pseudomonas aeruginosa and Staphylococcus aureus by a combination of spatial single-cell RNA sequencing and time-lapse imaging. We will characterize where and how the specialized phenotypic subpopulations emerge at different stages of biofilm development and how they change in response to competing species. In the second project, we will interrogate the functional role of the intermittent and heterogeneous activation of diverse metabolic and stress response pathways which we have observed even in isogenic cells and in absence of external cues. Specifically, we will test the hypothesis that heterogeneous sampling of such states may promote inter-species interactions with bacterial partners evolved to coexist in the same environment. In this project, I aim to uncover the phenotypic subpopulations arising in key species from human gut microbiota grown either solo or in pair-wise combinations with other co-occurring species. With these data, we will use gene regulatory network modeling to predict the higher-order interactions between gut microbiota species and engineer higher complexity consortia with predictable behavioral traits. The results will pave the way toward building systems- level understanding of the phenotypic structure and the emergent properties of a higher complexity natural microbiota. Overall, the developed approaches will become widely applicable tools for microbiological research and the acquired data will provide a foundation for high-resolution functional analyses of microbiota and biofilms.