Nascent biofilms on soft surfaces

TL;DR

The results identify that anisotropic drag forces on soft substrates, emerging due to local deformations, underpin colony anisotropy and swift verticalisation, while reduced drag on hard surfaces allows rapid expansion of monolayers, thus delaying the transition to a multilayer structure.

Abstract

Soft surfaces, spanning vastly different environmental and biomedical settings, are frequently colonised by surface-associated bacteria. Yet, how soft surfaces govern bacterial dynamics and their self-organisation into colonies remains poorly understood. Using experiments and agent-based modelling, we report the self-organisation of bacterial cells into nascent biofilms on soft substrates. By tuning the elastic modulus over two orders of magnitude, we show that the colony morphology, spreading dynamics and collective behaviour depend on the substrate stiffness, wherein softer surfaces promote slowly expanding, geometrically anisotropic, multilayered colonies, while harder substrates drive rapid, isotropic expansion of bacterial monolayers before multilayer structures emerge. Supported a cell mechanical model and two-dimensional agent-based simulations, our results identify that anisotropic drag forces on soft substrates, emerging due to local deformations, underpin colony anisotropy and swift verticalisation. In contrast, reduced drag on hard surfaces allows rapid expansion of monolayers, thus delaying the transition to a multilayer structure. Surface compliance, a key but overlooked determinant of early-stage biofilm development, could be harnessed to engineer biofilm structures and dynamics for nature-inspired and biomedical applications.

Figures (7)

• Figure 1: Morphology of growing E. coli colonies on low melting agarose pads, with increasing substrate stiffness. (A) Snapshots at 6 hours of colony growth on substrates having different concentration of low-melting-point agarose (LMP agarose), as captioned in the images. Here $x\%$ denote $x$ g of LMP agarose in 100 ml of LB media. (B) Colony area is plotted as a function of time. (C) Mean colony radius is plotted as a function of time, (D) The major (blue) and minor (orange) axes of the colonies at MTMT is shown. A significant difference is noted when the Young's modulus of the substrate increases from 21 kPa (low) to 92.4 kPa (high). Data represent at least three biological replicates for each stiffness condition ($n \geq 3$). Statistical difference tested for two sample t-test, p-value $<$ 0.01.
• Figure 2: Agent-based simulations confirm colony growth dynamics in experiments. (A) Simulation snapshots at a fixed time point for increasing substrate softness parameter $V_0$, with particles colored according to the magnitude of the parallel force they experience. (B) Colony area $A(t)$ as a function of time for different values of $V_0$. (C) Mean particle density $\bar{\rho}(t)$ over time, showing increased compaction for larger $V_0$. (D) Mean length $\bar{l}(t)$ over time, demonstrating that increased density on softer surfaces leads to shorter average cell lengths.
• Figure 3: Substrate stiffness impacts the growth of bacterial colonies. Various doubling times are reported here: (A) area doubling time, (B) cell number doubling time, (C) Mean elongation rate of cells, and (D) Growth rate of voids in the colony. Comparisons are made to check the variation in values when stiffness of the substrate is changed. A significant difference in area doubling time and void growth rate values is observed when the Young's modulus varies from from low to high values. Data represent at least three biological replicates per stiffness condition for statistical analysis. Asterisks correspond to a specific level of significance: two sample t-test, p-value$<$ 0.05 ($^*$); and p-value $<$ 0.01 ($^{**}$).
• Figure 4: Emergence of colony anisotropy captured by agent-based simulations. (A) The two eigenvalues of the gyration tensor of colonies over time. The minor eigenvalue $\lambda_{\text{min}}$ is strongly suppressed for larger $V_0$, i.e., softer substrates. (B) Colony aspect ratio, calculated from the eigenvalues of the gyration tensor, as a function of time. (C) Average local nematic order parameter $S_\text{avg}(t)$ over time, showing that orientational alignment is better preserved on softer surfaces.
• Figure 5: Boundary roughness of the growing colonies quantified by fractal dimension. (A) Histogram of the acute angle between the boundary tangent and bacteria orientations. Most cells tend to align with the tangent of the colony boundary. However tangential order breaks at the colony boundary due to imbalance between growth-driven expansion forces (due to the expanding colony) and the opposing drag force from the substrate, leading to orientational disorder at the boundary. High fractal at softer substrate (Inset). (B) Box counting algorithm for computing the fractal dimension of the colony boundary: the colony boundary coordinates are computed and the image is then divided into grids, with grid size progressively increased, and the number of boxes intersecting the colony boundary counted. The fractal dimension in each case is computed by finding the slope of logarithm of the number of boxes containing part of the boundary and logarithm of the box size. (C) The fractal dimension is plotted (mean and standard deviation), for colonies at around transition time for increasing Young's modulus of the LMP agarose substrate.