Shared biophysical mechanisms determine early biofilm architecture development across different bacterial species

Abstract

Bacterial biofilms are among the most abundant multicellular structures on Earth and play essential roles in a wide range of ecological, medical, and industrial processes. However, general principles that govern the emergence of biofilm architecture across different species remain unknown. Here, we combine experiments, simulations, and statistical analysis to identify shared biophysical mechanisms that determine early biofilm architecture development at the single-cell level, for the species Vibrio cholerae, Escherichia coli, Salmonella enterica, and Pseudomonas aeruginosa grown as microcolonies in flow chambers. Our data-driven analysis reveals that despite the many molecular differences between these species, the biofilm architecture differences can be described by only 2 control parameters: cellular aspect ratio and cell density. Further experiments using single-species mutants for which the cell aspect ratio and the cell density are systematically varied, and mechanistic simulations show that tuning these 2 control parameters reproduces biofilm architectures of different species. Altogether, our results show that biofilm microcolony architecture is determined by mechanical cell–cell interactions, which are conserved across different species.

Mechanical Characterization and Single‐Cell Imaging of Bacterial Biofilms

“…In the future, we expect that the techniques discussed here will be widely applied to study additional biofilm‐forming species and generate a library of information, which could be explored using data mining and machine learning tools [63,66,67] . An interesting question that could be addressed by such data‐driven approach is whether different species share the same biophysical mechanism for biofilm formation; a recent study has taken the first step to address this question [66] .…”

“…In the future, we expect that the techniques discussed here will be widely applied to study additional biofilm-forming species and generate a library of information, which could be explored using data mining and machine learning tools. [63,66,67] An interesting question that could be addressed by such datadriven approach is whether different species share the same biophysical mechanism for biofilm formation; a recent study has taken the first step to address this question. [66] It will also be interesting to see how new light sheet imaging techniques could give information about various physicochemical properties of a biofilm not accessible so far, such as how individual cells change gene expression profile within a lineage.…”

“…[63,66,67] An interesting question that could be addressed by such datadriven approach is whether different species share the same biophysical mechanism for biofilm formation; a recent study has taken the first step to address this question. [66] It will also be interesting to see how new light sheet imaging techniques could give information about various physicochemical properties of a biofilm not accessible so far, such as how individual cells change gene expression profile within a lineage. Furthermore, application of the single-cell technique to polymicrobial biofilms will generate new knowledge regarding interspecies interactions; an interesting case has recently been reported in which Candida albicans and Streptococcus mutans form interkingdom assemblages in human saliva.…”

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

“…We, therefore, developed a workflow to accelerate our annotation procedure of bacterial cells in 3D images (Figure 2). For this, we first used a previously published segmentation pipeline (Hartmann et al, 2019), including an algorithmic improvement published recently (Jeckel et al, 2022), to perform a segmentation of a raw 3D biofilm image (Figure 2, step 1). This segmentation pipeline relies on classical image analysis methods (filtering, thresholding, watershed algorithm) and generates a good, but not perfect, instance segmentation result.…”

“…cholerae biofilms by Hartmann et al, 2019, and an improved version of this algorithm based on a better watershed seed selection (Jeckel et al, 2022). For each of these segmentations, we computed the deviations from the ground truth.…”