Konstantin Neuhaus

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.

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.

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.

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.

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.

The extracellular matrix is a defining feature of bacterial biofilms and provides structural stability to the community by binding cells to the surface and to each other. Transitions between bacterial biofilm initiation, growth, and dispersion require different regulatory programs, all of which result in modifications to the extracellular matrix composition, abundance, or functionality. However, the mechanisms by which individual cells in biofilms disengage from the matrix to enable their departure during biofilm dispersal are unclear. Here, we investigated active biofilm dispersal of Vibrio cholerae during nutrient starvation, resulting in the discovery of the conserved Vibrio biofilm dispersal regulator VbdR. We show that VbdR triggers biofilm dispersal by controlling cellular release from the biofilm matrix, which is achieved by inducing the retraction of the mannose-sensitive hemagglutinin (MSHA) type IV pili and the expression of a matrix protease IvaP. We further show that MSHA pili have numerous binding partners in the matrix and that the joint effect of MSHA pilus retraction and IvaP activity is necessary and sufficient for causing biofilm dispersal. These results highlight the crucial role of type IV pilus dynamics during biofilm dispersal and provide a new target for controlling V. cholerae biofilm abundance through the induction and manipulation of biofilm dispersal.