Project Details
Projekt
Resolving bacterial collectives on the single-cell level with flow cytometry, cell sorting, and multi-omics data science - Z-PROJECT
Subject Area
Metabolism, Biochemistry and Genetics of Microorganisms
Bioinformatics and Theoretical Biology
Microbial Ecology and Applied Microbiology
Bioinformatics and Theoretical Biology
Microbial Ecology and Applied Microbiology
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 503905203
Collective life forms consist of cells that display remarkable physiological and phenotypic properties and have overcome the limitation of solitary life by evolving emergent functions. The nature of these emergent functions depends on the distribution and physiological states of individual cells, as well as their interactions with one another and their environment. In the first phase, we established a close collaboration with the members of the SPP2389 and performed comprehensive FCM and FACS analyses. We developed a biopsy method that enabled sampling of cells from any location in a colony's biofilm, unravelling colony biogeography at single cell level. In the second phase, we will develop advanced cytometric protocols for single cell characterization. Cells of particular interest will be sorted and transferred for subsequent scRNA-Seq application in partner laboratories using the biopsy method, flow cytometry, and FACS. We will integrate multi-parametric flow cytometry with subpopulation RNA-Seq to provide a robust (bulk) RNA-Seq approach for specific phenotypic cell subgroups that may exhibit distinct gene expression patterns. Both methods are expected to reveal high-resolution functional information about phenotypes in specific locations of a biofilm colony and will be key to elucidating emergent functions and organization of multicellular bacterial collectives. Complementing the experimental advances in the first phase, we developed statistical and data science workflows for the analysis of bacterial single-cell measurements. Our first contribution was an end-to-end framework, BacSC, tailored toward bacterial scRNA-Seq data. BacSC enabled statistically valid identification of bacterial heterogeneity on transcriptomic level for several bacterial species. BacSC enables SPP2389 members to perform scRNA-Seq data analysis with minimal external advice. Our second contribution was the design of biscot, a framework for multimodal bacterial single-cell data integration. biscot uses concepts from optimal transport to align FCM-defined single cell phenotypes with unpaired scRNA-Seq gene expression profiles, enabling the molecular characterization of phenotypes. In the second phase, we will refine the optimal transport framework and integrate other data modalities. To facilitate broader access and data interpretation, we will also initiative the creation of a publicly available data collection of FCM and scRNA-seq data, the Bacterial Single-Cell atlas (BacSCat). All methodologies developed in this Z-project will be made publicly available to encourage application in microbiology and computational biology research. The development of these techniques (and their dissemination within SPP2389) is a central pillar of this Z-project, and joint development of novel experimental and data science techniques will ultimately help decipher single-cell specific and emergent collective functions of bacterial macroscopic structures in a spatiotemporal context.
DFG Programme
Priority Programmes
