Density-Dependent Transition in Bacterial Self-Organization Driven by Confinement and Aerotaxis

Abstract

We experimentally investigate how aerotactic bacteria, confined within a thin liquid film between two solid substrates, respond to a controlled oxygen gradient. We find that the total bacterial number density dictates which mechanism dominates the steady-state spatial distribution: wall accumulation or aerotaxis. At low densities, despite receiving oxygen only from one substrate, motile bacteria accumulate at both walls, forming a symmetric distribution. In contrast, pronounced aerotactic migration toward the oxygen-supplying wall emerges as the density increases. Analyzing the temporal evolution of this bacterial distribution reveals that the aerotactic response is driven by a self-generated oxygen gradient induced by collective respiration. Our diffusion-advection model of bacteria and oxygen, accounting for aerotactic migration, hydrodynamic attraction to the walls, and respiration, quantitatively reproduces our experimental observations and provides valuable insights into bacterial self-organization within complex environments.

Figures (4)

• Figure 1: Experimental setup and steady-state bacterial distributions for different number densities. (a) Left: A schematic illustrating the confinement geometry. Bacteria are confined in a thin liquid film of height $h$ between a top oxygen-permeable coverslip and a bottom impermeable substrate. Right: A representative binary image of bacteria at high density near the oxygen-permeable top boundary. The scale bar is 100 . (b) Probability distribution functions (PDFs) of bacterial counts at steady state for three different number densities The PDFs are plotted against the normalized vertical position, $y/h$. The error bars represent the standard deviation of the temporal fluctuations in the bacterial count at each position, calculated over the recording period.
• Figure 2: Temporal evolution of the bacterial distribution at high density and a schematic of the system dynamics. (a) PDFs of bacterial counts for a high-density suspension ($11.7\times10^7$ cells/) measured at different time points, from 10 min (light gray triangles) to 60 min (black triangles). The error bars represent the standard deviation of temporal fluctuations in the bacterial count at each position, calculated over the recording period. (b) Schematic diagrams contrasting the system's evolution at high and low number densities. The top row depicts the high-density case, showing a transition from an initial symmetric distribution to an asymmetric steady state. The bottom row depicts the low-density case, showing the persistence of a symmetric distribution from the initial to the steady state. The color fill in the diagrams represents the oxygen concentration, which is defined by the legend in the top-right corner (labeled $\mathrm{O}_2$); blue and white indicate high and low concentrations, respectively.
• Figure 3: Comparison between experimental bacterial distributions (symbols) and model predictions (solid lines) for low and high number densities. Two representative bacterial number densities in Fig. \ref{['fig:sptial']} are chosen for numerical fitting.
• Figure 4: The state diagram of bacterial distribution symmetry as a function of total bacterial number density ($B_{\mathrm{tot}}$) and film height ($h$). The color scale shows the symmetry parameter ($sym$); darker colors indicate more asymmetric (aerotaxis-dominated) profiles. Regions of $sym<0.17$ are omitted because the numerical solutions fail to satisfy the boundary conditions in this highly asymmetric regime. Experimental data are overlaid as circles which are color-coded with the experimentally estimated $sym$.