Biofilm formation on human immune cells is a multicellular predation strategy of Vibrio cholerae.

Biofilm formation is generally recognized as a bacterial defense mechanism against environmental threats, including antibiotics, bacteriophages, and leukocytes of the human immune system. Here, we show that for the human pathogen Vibrio cholerae, biofilm formation is not only a protective trait but also an aggressive trait to collectively predate different immune cells. We find that V. cholerae forms biofilms on the eukaryotic cell surface using an extracellular matrix comprising primarily mannose-sensitive hemagglutinin pili, toxin-coregulated pili, and the secreted colonization factor TcpF, which differs from the matrix composition of biofilms on other surfaces. These biofilms encase immune cells and establish a high local concentration of a secreted hemolysin to kill the immune cells before the biofilms disperse in a c-di-GMP-dependent manner. Together, these results uncover how bacteria employ biofilm formation as a multicellular strategy to invert the typical relationship between human immune cells as the hunters and bacteria as the hunted.

Single-cell segmentation in bacterial biofilms with an optimized deep learning method enables tracking of cell lineages and measurements of growth rates.

Bacteria often grow into matrix-encased three-dimensional (3D) biofilm communities, which can be imaged at cellular resolution using confocal microscopy. From these 3D images, measurements of single-cell properties with high spatiotemporal resolution are required to investigate cellular heterogeneity and dynamical processes inside biofilms. However, the required measurements rely on the automated segmentation of bacterial cells in 3D images, which is a technical challenge. To improve the accuracy of single-cell segmentation in 3D biofilms, we first evaluated recent classical and deep learning segmentation algorithms. We then extended StarDist, a state-of-the-art deep learning algorithm, by optimizing the post-processing for bacteria, which resulted in the most accurate segmentation results for biofilms among all investigated algorithms. To generate the large 3D training dataset required for deep learning, we developed an iterative process of automated segmentation followed by semi-manual correction, resulting in >18,000 annotated Vibrio cholerae cells in 3D images. We demonstrate that this large training dataset and the neural network with optimized post-processing yield accurate segmentation results for biofilms of different species and on biofilm images from different microscopes. Finally, we used the accurate single-cell segmentation results to track cell lineages in biofilms and to perform spatiotemporal measurements of single-cell growth rates during biofilm development.

In Vivo Microrheology Reveals Local Elastic and Plastic Responses Inside 3D Bacterial Biofilms.

Bacterial biofilms are highly abundant 3D living materials capable of performing complex biomechanical and biochemical functions, including programmable growth, self-repair, filtration, and bioproduction. Methods to measure internal mechanical properties of biofilms in vivo with spatial resolution on the cellular scale have been lacking. Here, thousands of cells are tracked inside living 3D biofilms of the bacterium Vibrio cholerae during and after the application of shear stress, for a wide range of stress amplitudes, periods, and biofilm sizes, which revealed anisotropic elastic and plastic responses of both cell displacements and cell reorientations. Using cellular tracking to infer parameters of a general mechanical model, spatially-resolved measurements of the elastic modulus inside the biofilm are obtained, which correlate with the spatial distribution of the polysaccharides within the biofilm matrix. The noninvasive microrheology and force-inference approach introduced here provides a general framework for studying mechanical properties with high spatial resolution in living materials.

Simultaneous spatiotemporal transcriptomics and microscopy of Bacillus subtilis swarm development reveal cooperation across generations.

Development of microbial communities is a complex multiscale phenomenon with wide-ranging biomedical and ecological implications. How biological and physical processes determine emergent spatial structures in microbial communities remains poorly understood due to a lack of simultaneous measurements of gene expression and cellular behaviour in space and time. Here we combined live-cell microscopy with a robotic arm for spatiotemporal sampling, which enabled us to simultaneously acquire phenotypic imaging data and spatiotemporal transcriptomes during Bacillus subtilis swarm development. Quantitative characterization of the spatiotemporal gene expression patterns revealed correlations with cellular and collective properties, and phenotypic subpopulations. By integrating these data with spatiotemporal metabolome measurements, we discovered a spatiotemporal cross-feeding mechanism fuelling swarm development: during their migration, earlier generations deposit metabolites which are consumed by later generations that swarm across the same location. These results highlight the importance of spatiotemporal effects during the emergence of phenotypic subpopulations and their interactions in bacterial communities.

Single-objective high-resolution confocal light sheet fluorescence microscopy for standard biological sample geometries.

Three-dimensional fluorescence-based imaging of living cells and organisms requires the sample to be exposed to substantial excitation illumination energy, typically causing phototoxicity and photobleaching. Light sheet fluorescence microscopy dramatically reduces phototoxicity, yet most implementations are limited to objective lenses with low numerical aperture and particular sample geometries that are built for specific biological systems. To overcome these limitations, we developed a single-objective light sheet fluorescence system for biological imaging based on axial plane optical microscopy and digital confocal slit detection, using either Bessel or Gaussian beam shapes. Compared to spinning disk confocal microscopy, this system displays similar optical resolution, but a significantly reduced photobleaching at the same signal level. This single-objective light sheet technique is built as an add-on module for standard research microscopes and the technique is compatible with high-numerical aperture oil immersion objectives and standard samples mounted on coverslips. We demonstrate the performance of this technique by imaging three-dimensional dynamic processes, including bacterial biofilm dispersal, the response of biofilms to osmotic shocks, and macrophage phagocytosis of bacterial cells.

Spatial alanine metabolism determines local growth dynamics of Escherichia coli colonies.

Bacteria commonly live in spatially structured biofilm assemblages, which are encased by an extracellular matrix. Metabolic activity of the cells inside biofilms causes gradients in local environmental conditions, which leads to the emergence of physiologically differentiated subpopulations. Information about the properties and spatial arrangement of such metabolic subpopulations, as well as their interaction strength and interaction length scales are lacking, even for model systems like Escherichia coli colony biofilms grown on agar-solidified media. Here, we use an unbiased approach, based on temporal and spatial transcriptome and metabolome data acquired during E. coli colony biofilm growth, to study the spatial organization of metabolism. We discovered that alanine displays a unique pattern among amino acids and that alanine metabolism is spatially and temporally heterogeneous. At the anoxic base of the colony, where carbon and nitrogen sources are abundant, cells secrete alanine via the transporter AlaE. In contrast, cells utilize alanine as a carbon and nitrogen source in the oxic nutrient-deprived region at the colony mid-height, via the enzymes DadA and DadX. This spatially structured alanine cross-feeding influences cellular viability and growth in the cross-feeding-dependent region, which shapes the overall colony morphology. More generally, our results on this precisely controllable biofilm model system demonstrate a remarkable spatiotemporal complexity of metabolism in biofilms. A better characterization of the spatiotemporal metabolic heterogeneities and dependencies is essential for understanding the physiology, architecture, and function of biofilms.