(C) PLOS One
This story was originally published by PLOS One and is unaltered.
 . . . . . . . . . .



Shared biophysical mechanisms determine early biofilm architecture development across different bacterial species [1]

['Hannah Jeckel', 'Biozentrum', 'University Of Basel', 'Basel', 'Department Of Physics', 'Philipps-Universität Marburg', 'Marburg', 'Francisco Díaz-Pascual', 'Max Planck Institute For Terrestrial Microbiology', 'Dominic J. Skinner']

Date: 2022-11 

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.

Funding: This research was supported by grants from the European Union's Horizon 2020 European Research Council (716734 to KD), the Horizon 2020 Marie Skłodowska-Curie Innovative Training Network PHYMOT (955910 to KD), the Deutsche Forschungsgemeinschaft (DR 982/5-1 to KD), the Minna-James-Heineman-Stiftung (to KD; https://www.heinemanstiftung.org/ ), Bundesministerium für Bildung und Forschung (TARGET-Biofilms to KD; https://www.bmbf.de/bmbf/de/home/home_node.html ), the National Center of Competence in Research AntiResist funded by the Swiss National Science Foundation (51NF40 180541 to KD), the National Science Foundation (Award DMS-1952706 to JD), the Sloan Foundation (G-2021-16758 to JD), and by a grant from the MIT Mathematics Robert E. Collins Distinguished Scholar Fund (to JD; https://math.mit.edu/ ). In addition, this research was funded by scholarships from the Studienstiftung des deutschen Volkes (to HJ; https://www.studienstiftung.de/ ) and the Joachim Herz Stiftung (to HJ; https://www.joachim-herz-stiftung.de/en/ ), and by a Mathworks Fellowship (to DJS; https://science.mit.edu/resource/mathworks-fellowship/ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2022 Jeckel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Through the quantitative biophysical analysis methodology outlined above, we find that emergent architectural differences across biofilms of different species correlate with variations in cell shape and local cell density. To test whether these correlations are due to causal relationships, we used mutants of a single species and particle-based computational modeling to independently explore the biophysical phase space of early-stage biofilm architectures. These experiments and simulations showed that 2 mechanical parameters (cell aspect ratio and the cell–cell attraction) jointly determine the emergent biofilm architecture across different species, which reveals a conserved principle for architecture development of biofilm microcolonies.

To tackle this problem, we report here a combined experimental and theoretical investigation of three-dimensional (3D) biofilm architectures for the bacterial species, Vibrio cholerae, Escherichia coli, Salmonella enterica, and Pseudomonas aeruginosa, which are grown in microfluidic flow chambers as microcolonies up to a cell number of approximately 2,000 cells. Each of these species displays different growth characteristics, extracellular matrix components, cell morphology, and biofilm architectures. To identify common architectural characteristics across different bacterial species and to ultimately identify conserved biophysical principles for biofilm development, it is necessary to have quantitative metrics enabling comparisons between multicellular structures, which are able to robustly distinguish different biofilm architectures. Building on recent tools for 3D biofilm image analysis [ 41 ], we are able to extract and quantify numerous single-cell properties and emergent collective properties from microscopy image data of individual biofilms. To analyze these measurements, we introduce here a statistical metric framework based on a general Chebyshev representation of the experimentally measured parameter distributions, which is able to distinguish different biofilm species based on their architectural features. This metric overcomes limitations of previous methods that relied on the assumption of normally distributed data [ 21 ]. Since the underlying mathematical formulations of our analysis framework of 3D multicellular structures is generic, the method will be broadly applicable to other prokaryotic and eukaryotic multicellular structures in the future.

Despite the molecular dissimilarities, biofilms of different species generally share a robustness against mechanical and chemical perturbations, and it is not well understood how these multicellular properties of biofilms arise from the collective growth and spatiotemporal self-organization of the communities. Recent advances in live imaging techniques make it possible to observe the development of early-stage biofilms at single-cell resolution, starting from a single founder cell up to a few thousand cells [ 13 , 19 – 22 ]. Imaging-based studies have provided key insights into the importance of mechanical cell interactions [ 21 , 23 – 30 ], cell surface attachment [ 25 , 31 – 35 ], growth memory [ 22 ], external fluid flow [ 13 , 36 , 37 ], and the external mechanical environment [ 38 – 40 ] for the emergent architecture in biofilms. However, these studies were restricted to a single species and it remains an open question whether there are common biophysical principles that govern biofilm architecture development across species.

Bacterial biofilms are multicellular communities that grow on surfaces within a self-produced extracellular matrix [ 1 , 2 ]. Major research efforts over the past 2 decades [ 3 – 7 ] have established the ecological, biomedical, and industrial importance of bacterial biofilms and revealed that biofilms are highly abundant on Earth [ 8 ]. They are formed by many different species, in a multitude of different environments on many different types of interfaces. This diversity is reflected in the resulting biofilm architectures, which range from microscopic cell aggregates to macroscopic colonies, and to thick mats of cells that cover surfaces [ 1 , 5 , 8 ]. Biofilm architecture is impacted by a variety of external and internal cues including the nutritional environment [ 9 , 10 ], shear flow [ 11 – 13 ], motility and quorum sensing properties of the biofilm-forming strain [ 9 , 14 , 15 ], as well as the composition and properties of the extracellular matrix, which varies widely between different species [ 7 , 16 – 18 ].

Results and discussion

Quantifying early-stage biofilm architecture across species To investigate the structural differences between biofilm architectures within and across bacterial species, we performed single-cell resolution imaging. For each of the 4 species, E. coli, V. cholerae, P. aeruginosa, and S. enterica, 15 biofilms were grown in microfluidic flow chambers from a few surface-attached founder cells until they reached around 2,000 cells, followed by imaging using confocal microscopy (Fig 1A; Materials and methods). For these biofilm sizes, the cells are expected to grow exponentially throughout the microcolonies [21]. Although all species formed colonies, the biofilm architectures of the 4 species were qualitatively different (Fig 1A). To quantify the observed differences in biofilm shape and structure between species, we segmented all individual cells in all biofilms following [21]. Using the software tool BiofilmQ [41], we measured for each biofilm several single-cell properties such as cell length, cell diameter, and cell convexity, together with emergent collective properties, such as local cell number density and nematic order, resulting in a histogram for every one of the m = 16 measured properties (see complete list in Table D, Section B in S1 Text). Each biofilm is thus represented by a set of m histograms. PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 1. Early-stage biofilm architectures of different bacterial species can be quantitatively distinguished within a two-dimensional phase diagram derived from a statistical analysis of architectural properties. (A) Representative 3D biofilm microcolony architectures of 4 bacterial species reconstructed from segmented confocal microscopy images at comparable cell numbers (approximately 2,000 cells). Each cell is colored according to the local density within its neighborhood, of radius 2 μm. Scale bars, 5 μm. (B) For each biofilm, we approximated the distributions of 16 measured properties with Chebyshev polynomials (Section C in S1 Text). Using the Chebyshev polynomials for each measured property, a Cd measure is defined (Section C in S1 Text). Using this measure, highly correlated properties are identified and reduced as indicated by red squares, leaving p = 13 relevant properties. (C) The Cd also provides a robust and quantitative comparison of biofilm architectures from different species, as indicated by the block structure in this diagram. (D) PCA based on the Chebyshev coefficient space (Section C in S1 Text) robustly distinguishes biofilms of the 4 different species, E. coli, V. cholerae, P. aeruginosa, and S. enterica. (E) Cell aspect ratio and local number of neighbors are the key contributors to the first principal component (Section C, Fig D in S1 Text). (F) Representing the experimental data in the mean aspect ratio vs. cell number density plane confirms that these 2 properties define a biophysically interpretable phase diagram to categorize biofilm architectures. Source data are available at DOI: 10.5281/zenodo.7077624. Cd, Chebyshev dissimilarity; PCA, principal component analysis; 3D, three-dimensional. https://doi.org/10.1371/journal.pbio.3001846.g001 Previous approaches have used mean- and variance-based measures of these histograms [21] to distinguish biofilm architecture; however, these measures do not carry information about the histogram’s shape and are therefore of limited utility. To broaden the scope of our statistical analysis and, therefore, the range of systems that it can be applied to, we sought a more general approach to systematically compare sets of histograms. To this end, we represented each empirically measured histogram with a Chebyshev polynomial of degree d = 20 using kernel density estimation (Section C, Fig A in S1 Text). Replacing approximately 2,000 single-cell measurements for each biofilm and each parameter with d + 1 = 21 polynomial coefficients allowed us to compress the experimentally observed data while retaining information about their distributions beyond mean values and variances. From a (d + 1) × m matrix containing all the Chebyshev coefficients for a given biofilm, we constructed a Chebyshev dissimilarity (Cd) measure, to compare 2 such matrices and, hence, 2 biofilms (Section C in S1 Text). Mathematically, Cd provides an upper bound on the cumulative L 1 -distance between collections of histograms. Similarly, taking a vector of Chebyshev coefficients constructed from a single property across all biofilms allows us to apply Cd to compare similarities of measured properties (Section C in S1 Text). Some properties, such as the cellular aspect ratio (the ratio of cell length to cell width) and cell length, can be expected to be closely related to each other and therefore add redundant information to the analysis. To prevent double-counting, we identified these highly correlated properties by performing clustering based on Cd and using the silhouette coefficient to determine the optimal cluster number (more details are provided in Section C in S1 Text). This analysis left us with p = 13 essential properties, which characterize biofilm architecture (Fig 1B). When calculating Cd for each pair of biofilms using the 13 essential properties, we observe a robust distinction according to species, as evident from the block structure in Fig 1C.

Data-driven identification of the phase diagram of early-stage biofilm architecture Principal component analysis (PCA) applied to the flattened (d + 1) × p = 21 × 13 dimensional vectors of Chebyshev coefficients representing each biofilm revealed that there are 4 distinct clusters corresponding to the 4 bacterial species (Fig 1D). The information contained in the p = 13 distributions of measured parameters is therefore sufficient to capture the key architectural differences between species. The first principal component, which explains more than 50% of the variation in the data, can be used as a scalar measure for biofilm architecture and will from here on be referred to as the biofilm architecture index (BAI). To investigate which of the measured properties could be responsible for the interspecies variation, we examined the contributions of each parameter to the BAI (Fig 1E). The feature that contributed most to the BAI is the local cell number density, defined as the number of neighbors that a cell has within a 2-μm radius. The second highest contributing feature was the cell aspect ratio. The prominent contributions of the cell number density and cell aspect ratio to the BAI suggest that variations in these 2 parameters across biofilms could be responsible for variation in the observed architectures. To verify that these 2 properties provide the basis for a suitable biophysical phase diagram of biofilm architecture, we plot each biofilm in the mean cell number density versus mean cell aspect ratio plane (Fig 1F). The clear separation of the 4 species in this two-dimensional phase space shows that biofilm architectures can be efficiently characterized by these 2 parameters. We note that classical liquid crystals can also be characterized by an aspect ratio versus number density phase diagram [42,43], which highlights an interesting analogy between passive nematic structures and growth-active nematic biofilms.

[END]
---
[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001846

Published and (C) by PLOS One
Content appears here under this condition or license: Creative Commons - Attribution BY 4.0.

via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/plosone/