Flagellar motility allows many bacteria like E. coli to randomly walk through the environment by alternating between straight flagella-powered runs and short turns called tumbles. They also bias this random walk to move towards favorable conditions (optimal temperature, pH, high concentration of nutrients, etc), thanks to the dedicated chemotaxis sensory pathway.
We are interested in the roles of flagellar motility and chemotaxis in population organization at several levels.
Physics of active system.
In this first line of research, we ask how the physics of cell swimming shape the complex organization of bacterial populations.
For this, we study the out-of-equilibrium processes reshaping the structure and dynamics of cell populations, which are powered by the mechanical energy released by swimming, and involve both steric (at contact) and hydrodynamic (via the fluid) interactions between swimmers and with non-motile cells and surfaces in the milieu.
Collective swimming – when the density of swimming cells is getting high, collective motion emerge, where the swimming cells form intermittent swirling packs of hundreds of cells (Figure 1 and movie). We have shown that under moderate confinement, these swirls emerge from hydrodynamic interactions between swimmers primarily, and that the swirls prevent the cells to perceive chemical gradient, by reducing the efficiency of the gradient sensing mechanism by temporal comparisons. We are interested in transpositions to other types of motility.
The cell suspension (10% volume fraction) is observed in a 50 µm high microfluidic channel at 10x magnification. The map of the local velocities (right) is measured by image velocimetry.
SCale bar 50 µm
Organization of binary mixtures – We are currently investigating self-organization in mixtures of motile and non-motile cells, as a simple model system of more complex bacterial community. We discovered that non-motile E. coli cells form large scale density patterns in presence of motile E. coli in microfluidic channels. We investigate:
- what is the mechanism of emergence of these patterns?
- how these density patterns affect biofilm formation?
- how externally imposed flows reorganize the population?
These studies have strong implication for understanding the physical principle behind the organization of multi-species bacterial communities, in static or flowing environments.
Effects of noisy signaling pathway.
This second line of research is interested in individuality in swimming behavior, featuring large fluctuations both from cell to cell and for one cell over time even in clonal populations.
During the run-and-tumble swimming of E. coli, the duration of the runs has been shown to fluctuate over time scales of tens to hundreds of seconds. The random walk therefore alternates large explorations and short local searches. We have recently shown how this behavior results from specific elements of the architecture of the chemotaxis pathway, the "sense of smell” associated with motility. This behavior has been theoretically argued to be an optimal exploration strategy of homogeneous environment for any flagellated bacterium and to improve the chemotactic behavior, despite having been so far reported only in E. coli. Moreover, the average behavior of single cells vary widely from one clone to the next in the population. THis has been shown to have strong effects on the performance of a population in and across differnet environments. We therefore are interested in the following questions:
- Is the fluctuating dynamics truly improving the chemotaxis in E. coli?
- Is this type of dynamics universal? How frequent is it among bacteria?
- How do the number of and interactions betwen the pathway component produce cell to cell variability?
Our lab contributed to the development of several image analysis methods useful for the study of bacterial motility.
Fourier image analysis methods for measuring bacterial dynamics – These methods are based in analyzing the evolution of the spatial features of the images over time to deduce the dynamics of the imaged objects (eg. Bacteria), and started being developed in the 2000’s by several pionniering labs (Trappe, Cerbino, Poon), with Differential Dynamic microscopy. We implemented these methods as plugins for imageJ, Available on Github.
Differential Dynamic microscopy (DDM): a method to measure swimming speed, diffusion, fraction of swimming cells and local cell density. First implemented by R. Cerbino and V. Trappe.
Phase Differential microscopy (PhiDM): a method to measure global drifts in the image. Useful for measuring chemotaxis drifts.
Local PhiDM: a local version of PhiDM, which allows to measure local drifts in the sample, and thus yields an image velocimetry measurement similar to but faster than particle image velocimetry (PIV).
Particle tracking – We provide an ImageJ PlugIn for particle tracking on demand, with many analysis modules to quantify and interpret swimming behavior.
Simulations – We developed several agent-based simulation models for swimming bacteria and growing yeast, available on GitHub.